Bipartite Graph Partitioning and Content-based Image Clustering
|
|
- Georgia Haynes
- 5 years ago
- Views:
Transcription
1 Bipartite Graph Partitioning and Content-based Image Clustering Guoping Qiu School of Computer Science The University of Nottingham cs.nott.ac.uk Abstract This paper presents a method to model the images and their content descriptors in large image databases using bipartite graphs. A graph partitioning algorithm is then developed to cluster the images and their content description features simultaneously such that each cluster is automatically associated with the set of features that best describes its visual contents. The association of features with image clusters enables semantic based search of image databases and the division of the database into visually aligned hierarchical groups facilitates fast content-based image retrieval. Introduction Managing large image repositories, making image and video items easily searchable is a very challenging problem. Content-based image indexing and retrieval (CBIR) is a popular approach to finding solutions to this problem []. In traditional CBIR techniques, low level image features, such as colour histogram, texture descriptors and others are used to represent image contents. Naïve approaches derive image features from the entire image, while more sophisticated approaches using advanced image segmentation techniques to divide image into meaningful regions or possible objects before deriving low level descriptors []. Once a set of content descriptors has been computed, pattern matching techniques are employed to compare the similarity between the query example and images in the database. Each image in the database is then given a score to indicate the degree of similarity between the image and the query image. Although many techniques have been proposed in the literature, many computational and user interface issues in CBIR remain very difficult. Most approaches use more than one type of visual features to represent the image content. Ultimately, a single score has to be computed from the multi-feature descriptors. In the case of colour histogram based approaches, for example, the most frequently used method for computing the similarity between images based on their colour histograms is to compute the L or L norms between the histograms. Statistics has shown that colour histograms of individual images tend to be sparse, with most of the pixels in any one image concentrated on a few colour bins. This means, certain colours are more important than others for certain images. However, the L or L norms do not give out information regarding which colour bins are more important to a given image. Each colour bin can be regarded as a different feature (colour). Knowing which feature is more important to a given image can be very useful in providing the semantics of the image, which can help more effective searching and browsing through the database. For example, a certain image s histogram has larger counts in the bluish colour bins, this means that the colour theme of the image is bluish. This bluish can be regarded as a semantic term of the image thus allowing a type of semantic based searching and browsing. This paper presents a novel approach to image database indexing and retrieval. We propose to use bipartite graph to model the images and their contents simultaneously. For the two sets of vertices of a bipartite graph, we use one set to associate with the image content descriptors and the other to associate with the images. The edges linking these two sets of vertices measure the degrees of associations between the content descriptors and the images. We then introduce graph partitioning to cut the graph such that images and the features that are most strongly associated with each other are clustered into the same groups. Each group formed in this way manifests a certain theme which is strongly linked to its associated features, which in turn provides the content semantics for the images, thus facilitating image indexing and retrieval. Graph partitioning has many very well established theoretical results, which makes the approach very attractive. Recent computer vision literature includes the use of graph partitioning for image segmentation [, 3]. In the non vision literature, graph theory was used quite extensively for document clustering [4, 5]. To the knowledge of the authors, this work represents the first time that graph partitioning is employed for the co-clustering of content descriptors and images for image database application. Although graph partition problem is NPcomplete, which will make graph based methods computationally unattractive, recent results have shown that graph partition can be efficiently computed by using spectral algorithms or eigenvector methods [-5]. Bipartite graph can also be partitioned by a Hopfield network [6]. In this work, we develop a
2 Hopfield network based solution for the partitioning of bipartite graph of image database. The organisation of the paper is as follows. In the following section we present the use of bipartite graphs for the modelling of image databases. Section 3 present a Hopfield network based approach to the partitioning of the image database bipartite graph. Section 4 presents experimental results and section 5 conclude the paper. Modelling Image Database using Bipartite Graph. Bipartite Graph and Its Partitioning A graph G = (V, E) is a set of vertices V = {,,, V } and a set of edges {k, l} each with the edge weight E kl. A graph G = (V, E) is a bipartite graph if it consists of two classes of vertex, X and Y, V = X Y, X Y =, and each edge in E has one endpoint in X and the other endpoint in Y. We denote an undirected bipartite graph by the triple G = (X, Y, W), where X = {x, x, x m }, Y = {y, y, y n }, W = {w kl }, where w kl > 0 is the weight between vertices k and l, w kl = 0 if there is no edge between vertices k and l. We consider the case of bipartitioning the bipartite graph, where the vertices of X are partitioned into two sub sets X = X X, and simultaneously, the vertices of Y are also partitioned into two sub sets Y = Y Y. Depending on the applications, different graph partitioning criterions can be introduced to partition the vertices. In graph terminology, the partitioning is measured by Cuts. The criterions that have been used in the literature include Min Cut, Min Max Cut, Ratio Cut and Normalized Cut [-5]. For these criterions, spectral graph partitioning algorithms have been developed to compute the cuts efficiently. For the sake of concise presentation, we omit the formal definitions and readers are referred to various references for details. Another type of approaches to graph partitioning is to use neural network models [6]. In this method, a socalled computational energy function is defined and iterative processes are used to optimise the computational function. In this paper, we develop a bipartite partitioning algorithm for image database modelling application based on the later approach.. Content-based Image Indexing and Retrieval Content-based image indexing and retrieval is an important area in which computer vision and image processing play a significant role. But unlike classical vision tasks, such as object recognition, the demand of a CBIR system on vision techniques is less precise. Although many current vision algorithms are either not stable or restricted in very narrow application domains, they are sufficient in providing useful solutions to a CBIR system. Although various approaches have been proposed to represent image contents, techniques based on first order statistics, or histograms of low level features, such as colour histogram, colour correlogram, and MPEG-7 colour structure histogram [], are popular and have been found to be effective. For presentation purpose, we will use colour histogram based content descriptor in our discussion. Extension to other descriptors is straightforward. Let H = {h(c i )}, where h(c i ) is the count of occurrences of the ith colour c i. Each bin in the histogram is associated with a certain low-level properties, in this case colours. The value of h(c i ) indicates an association of the colour c i with the image. Roughly speaking, a larger value indicates that there are more pixels in the image have colours close to that colour, and that colour is more important in relation to the visual appearance of the image. In traditional content based image retrieval, image similarity is based on either L or L norms of histograms. The relative importance of each colour bin to an image is not explicitly used. Knowing which low level features (colours) are more strongly associated with an image can provide useful information to search for images. For example, if the bluish bins of an image s histogram have large counts, then it can be reasoned that the image contains bluish colour scenes. This bluish colour theme can be regarded as a semantic term of that image, which will in turn enable semantic based image retrieval. How the semantics can be used for more effective image database management is not the topic of the current paper. The contribution of this paper is to present a method to associate low level features with images based on a novel graph partitioning approach..3 Simultaneous Modelling Contents and Images We wish to model image content descriptors and images of an image database simultaneously using a bipartite graph G = (X, Y, W). Assuming each image in the database is represented by an m-bin colour histogram, and H l = (h l (c ), h l (c ),, h l (c m )), denotes the histogram of the lth image, where l =,, n. A straightforward mapping of the colours, the histograms and the images to G = (X, Y, W) is as follows: X = (c, c,, c m ), i.e., each vertex in X corresponds to a colour. Y = {y, y, y n }, and y l represents the lth image in the database. {w kl } = { h l (c k ) }, k =,,, m, l =,,, n, i.e., the weight of the edge (k, l) is the kth colour bin count of the lth image. In this way, an image database is completely characterised by the bipartite graph G = (X, Y, W). In the next section, we present an algorithm to partition
3 the graph such that images and their most important features are clustered simultaneously. 3 A Bipartite Graph Partitioning Algorithm for Image Database Clustering Graph partition is in general NP-complete. However, recent research using eigenvector or spectral methods for graph partitioning have demonstrated that the problem can be solved quite efficiently. A key factor that affects the quality of the solution is the cut criterions used. This criterion is inevitably task dependent. We introduce criterions that are suitable for image database applications [-5]. A cut partitions X = X X, and Y = Y Y. Let us assume that X is paired with Y and X paired with Y. In our current setting, this means, the colours being partitioned into X are more strongly associated with images that are partitioned into Y, and the relations between X and Y are similarly defined. The following objective function defines a reasonable criterion for the partitioning J = AS AS( X, Y ) + AS( X, Y ) ( X, Y ) AS( X, Y ) where AS( X, Y ) = w kl k X, l Y and other three terms are similarly defined. () Maximising J is equivalent to maximising the first two terms and minimising the last two terms. The meaning of the criterion can be easily understood. Maximising AS(X, Y ) means that images partitioned into sub set Y are strongly associated with colours being partitioned into the sub set X. Maximising AS(X, Y ) has similar explanation. Minimising AS(X, Y ) means that image partitioned into sub set Y are least associated with colours being partitioned into the sub set X. Minimising AS(X, Y ) can be understood similarly. Although () is a sensible criterion, it could produce unbalanced cut in the sense that the size of any of the sub sets, X, X, Y and Y could be very small even empty, because it can be easily shown that the criterion of () is equivalent to Min Cut in graph theory. Whether other criterions, Ratio Cut, Min Max Cut and Normalized Cut will suit our current application needs further study. We here introduce another objective function which will produced a more balanced partition. First let us define a weight for each of the vertices in X and Y w k = w w l = w () x ( ) kl y ( ) l If the histograms are normalised, then w y (l)=. We then define the following objective function k kl J = λj λ ( ) ( ) wx k wx k k X k X (3) λ 3 () () wy l wy l l Y l Y where λ, λ and λ 3 are non-negative weighting constants. The new objective function is based on J and two new terms. The physical meaning of the first new terms is that, we want to partition the colours in such a way that, the total number of pixels accumulated over the whole database, should split equally between the two groups of colours. If the database is large, this condition makes reasonable statistical sense. The second new term in fact favours the two groups Y and Y have equal number of images (if all histograms are normalised). This second new term is somewhat artificial. We will investigate and explain its impacts on the partition in the next section. 3. A Neural Network based Graph Partitioning Algorithm In order to partition the graph in such a way the J is maximised, we here present a solution based on the Hopfield neural computational models [6]. We assign a binary variable to each vertex and for convenience using the following notations: x k = + if x k X, x k = - if x k X, y l = + if y l Y,, y l = - if y l Y, for k, l. We now re-write (3) in terms of x k and y l : J m n = λ xk yl wkl λ ) k = l= k = l = m n ( xk wx ( k) ) λ3 ( yl wy ( l ) (4) then, J in (4) can be optimised by a Hopfield neural model. However, one difficulty of (4) is that the three terms each has different importance, which have to be determined a priori by using appropriate weighting constants (this is a general a difficult problem in computer vision and pattern recognition and generally no systematic solutions are available). To avoid this we decided to optimise each term in (4) in turn. In order to prevent the algorithm from getting trapped in local minima, we optimise the terms in a stochastic manner. Using the Hopfield network, we have the algorithm described in pseudocode in the algorithm box. Briefly, the algorithm first assigns random numbers to the states of the vertices. It then picks a random vertex from X and updates its state in such a way that the first term in (4) is increased. It then picks a random vertex from Y and updates it in such as way that the first term in (4) is increased. If the second term is used in the partition, then the algorithm picks another random vertex from X, this time the state of the vertex is updated to increase the second term in (4). If the third term is used in the partitioning, then a random vertex from Y is picked and its state updated such that the third term in (4) is increased. This process is repeated until either a pre-
4 set maximum number of iterations is reached or until further changes in the vertices states do not changes the objective function s value. Prco Hopfield Network Bipartite Graph Bipartitioning Algorithm for k = 0 to m x[k] = random (-, ) //random number between and + for l = 0 to n y[l] = random (-, ) //random number between and + while (not converge or less than Max Iterations) do //Pick a random vertex from X, and update its state in such a way that the first term in (4) is increased k = random (m) // a random between 0 and m H[k] = 0 for l =0 to n H[k] += - y[l]*w[k][l] //w[k][l] = w kl if H[k] 0 then x[k] = + else x[k] = - //Pick a random vertex from Y, and update its state in such a way that the first term in (4) is increased l = random (n) // a random between 0 and n H[l] = 0 for k =0 to m H[l] += - x[k]*w[k][l] //w[k][l] = w kl if H[l] 0 then y[l] = + else y[l] = - // If λ 0, pick a random vertex from X and update its state such that this term is increased. k = random (m) // a random between 0 and m H[k] = 0 for l =0 to m H[k] += x[l]*wx[l] // wx[l] = w x (l) if H[k] 0 then x[k] = + else x[k] = - // If λ 3 0, pick a random vertex from Y and update its state such that this term is increased. End while End Proc l = random (n) // a random between 0 and n H[l] = 0 for k =0 to n H[l] += y[k]*wy[k] // wy[k] = w y (k) if H[l] 0 then y[l] = + else y[l] = - 4 Experimental Results We have applied the graph partitioning method to image database clustering. In the implementation, we use a simple frequency classified colour histogram descriptor to represent the content of each image. Each histogram consists of 56 bins divided into 4 bands each band consists of the same 64 colours. Each band collects pixels from image regions of different frequencies. The first 64 bins counts the colours occur in the low frequency (smooth) regions, the second 64 bins counts colours occur in the next higher frequency band and so on. This way the counts in the bins not only reflect the colour but also texture properties of the image as well. If most of the counts concentrated in the first 64 bins, then the image is mostly smooth, conversely, if the counts are concentrated in the last 64 bins, then the image contains very busy texture surfaces.
5 (00) (0) Bin Map (00) Bin Map (0) Figure The first level groups partitioned by the proposed algorithm. Also shown are associated histogram bins. Each row of the bin map consists of 64 colours in each frequency band. An empty (white) block indicates that colour is not associated with that group. For each image in the database, a 56 bin histogram is constructed. We then apply the bipartite graph partitioning algorithm to cluster the histogram bins and the images simultaneously in a recursively manner. The algorithm is first applied to the entire database to divide it into two groups. The resultant groups are then partitioned again. In each subsequent application of the algorithm, all the bin counts of the histograms of the images in each sub group were used instead only those bins that are associated with the group in previous round of partitioning. It can be easily understood that this way, the images in the database can be put into a binary tree data structure with each node holds information about the images and their associated colour bins. These colour bins contains information about the nature of the contents of those images held in that node, which can in turn be used for content based image retrieval, either based on the semantics of those images, e.g., the colour themes of the images and the texture roughness of the images, or based on query by example paradigm. Putting the database in a binary tree will help fast search. One possible search strategy could be, basing on the bin partition at each node, branching to the next level based on which set of bins of the two groups contains more pixels in the query image. This is in contrast to traditional full search both in the sense of the use of the full histogram and search the entire database. This search method will definitely be faster than full database search. We also expect it will perform better because the features (colour bins) are used in a selective way. Work is currently underway to evaluate the potential of this technique for content-based image retrieval both based on a semantic approach and a query by example approach. Here we present results on the classification of two colour texture databases using the bipartite graph partitioning algorithm presented in this paper. Figures and show results of partitioning a 70-image colour texture database by a one side balanced cut (λ = λ =, λ 3 = 0, note that the actual values of the weighting constants are irrelevant if non-zero). Also shown are the colours associated with each cluster. It is seen that the groupings are visually similar and the bin maps have strong association with the appearance of the images in the groups. We mentioned previously that the third term in (3) and (4) are somewhat artificial because we force the partition to put equal number of images into each group without regarding to their actual contents. Figure 3 shows such a partitioning using all three terms in J. It is clearly seen that images in each group are somewhat similar, however, each group is less homogeneous and has many visual clutters as compared with cuts without the third term. Figure 4 shows 8 groups of images partitioned from another colour texture database consisted of 088 images (λ 3 = 0). If is again seen that each group contains image with similar visual attributes. From these results, it can be said that the algorithm has succeeded in clustering the features and images simultaneously. In our simulations, we set the iteration number to 5000 and it took less than minute on a Pentium 4 PC to cluster the 088 image database into 3 level binary tree (4 hierarchical clusters). 5 Concluding Remarks We have presented a method to model images and their content description features in a large image
6 database using bipartite graphs. We have also presented a method to partition the graph in a balanced and meaningful manner. Such model enables the simultaneous classification of images and their features, which in turn automatically associates images and their most important visual attributes. Such an association can facilitate both semantic based and query by example based image database search. References [] A. W. M. Smeulders et al, "Content-based image retrieval at the end of the early years", IEEE Trans PAMI, vol., pp , 000 [] Y. Weiss, Segmentation using eigenvectors: a unifying view, ICCV 999 [3] J. Shi and J. Malik, Normalized cut and image segmentation, IEEE PAMI, vol, pp , 000 [4] I. Dhillon, Co-clustering documents and words using bipartite spectral graph partitioing, ACM Knowledge Discovery Data Mining KDD 0, pp [5] C. Ding etal, A Min-max cut algorithm for graph partitioning and data clustering, IEEE st Conference on Data Mining, 00, pp [6] J. Hertz, R. G. Palmer and A. Koch, Introduction to the Theory of Neural Computation. Perseus Publishing, 99 (000) (00) (Bin Map 00) (Bin Map 000) (00) (0) (LCI Map 00) Bin Map (0) Figure, The second level 4 groups of images and their associated colour bin maps. For explanation of the colour bin map, see captions in Figure.
7 (00) (0) Bin Map (00) Bin Map (0) Figure 3, Two groups at the second level of partitioning based on all three terms of the objective function. For explanation of the colour bin map, see captions in Figure. (0000) (000) (0) (00) Figure 4 (part A), of the 8 visual groups at the 3 rd level of the partitioning hierarchy (continued)
8 (000) (00) (00) (0) Figure 4 (part B), 6 of the 8 visual groups at the 3 rd level of the partitioning hierarchy
Sequential Maximum Entropy Coding as Efficient Indexing for Rapid Navigation through Large Image Repositories
Sequential Maximum Entropy Coding as Efficient Indexing for Rapid Navigation through Large Image Repositories Guoping Qiu, Jeremy Morris and Xunli Fan School of Computer Science, The University of Nottingham
More informationVisual Representations for Machine Learning
Visual Representations for Machine Learning Spectral Clustering and Channel Representations Lecture 1 Spectral Clustering: introduction and confusion Michael Felsberg Klas Nordberg The Spectral Clustering
More informationContent-based Image and Video Retrieval. Image Segmentation
Content-based Image and Video Retrieval Vorlesung, SS 2011 Image Segmentation 2.5.2011 / 9.5.2011 Image Segmentation One of the key problem in computer vision Identification of homogenous region in the
More informationImage Segmentation for Image Object Extraction
Image Segmentation for Image Object Extraction Rohit Kamble, Keshav Kaul # Computer Department, Vishwakarma Institute of Information Technology, Pune kamble.rohit@hotmail.com, kaul.keshav@gmail.com ABSTRACT
More informationImproving Recognition through Object Sub-categorization
Improving Recognition through Object Sub-categorization Al Mansur and Yoshinori Kuno Graduate School of Science and Engineering, Saitama University, 255 Shimo-Okubo, Sakura-ku, Saitama-shi, Saitama 338-8570,
More informationData Clustering. Danushka Bollegala
Data Clustering Danushka Bollegala Outline Why cluster data? Clustering as unsupervised learning Clustering algorithms k-means, k-medoids agglomerative clustering Brown s clustering Spectral clustering
More informationCS 534: Computer Vision Segmentation II Graph Cuts and Image Segmentation
CS 534: Computer Vision Segmentation II Graph Cuts and Image Segmentation Spring 2005 Ahmed Elgammal Dept of Computer Science CS 534 Segmentation II - 1 Outlines What is Graph cuts Graph-based clustering
More informationEE 701 ROBOT VISION. Segmentation
EE 701 ROBOT VISION Regions and Image Segmentation Histogram-based Segmentation Automatic Thresholding K-means Clustering Spatial Coherence Merging and Splitting Graph Theoretic Segmentation Region Growing
More informationCombining Top-down and Bottom-up Segmentation
Combining Top-down and Bottom-up Segmentation Authors: Eran Borenstein, Eitan Sharon, Shimon Ullman Presenter: Collin McCarthy Introduction Goal Separate object from background Problems Inaccuracies Top-down
More informationImage Segmentation. Srikumar Ramalingam School of Computing University of Utah. Slides borrowed from Ross Whitaker
Image Segmentation Srikumar Ramalingam School of Computing University of Utah Slides borrowed from Ross Whitaker Segmentation Semantic Segmentation Indoor layout estimation What is Segmentation? Partitioning
More informationCS 534: Computer Vision Segmentation and Perceptual Grouping
CS 534: Computer Vision Segmentation and Perceptual Grouping Ahmed Elgammal Dept of Computer Science CS 534 Segmentation - 1 Outlines Mid-level vision What is segmentation Perceptual Grouping Segmentation
More informationAn Efficient Approach for Color Pattern Matching Using Image Mining
An Efficient Approach for Color Pattern Matching Using Image Mining * Manjot Kaur Navjot Kaur Master of Technology in Computer Science & Engineering, Sri Guru Granth Sahib World University, Fatehgarh Sahib,
More informationImproving Image Segmentation Quality Via Graph Theory
International Symposium on Computers & Informatics (ISCI 05) Improving Image Segmentation Quality Via Graph Theory Xiangxiang Li, Songhao Zhu School of Automatic, Nanjing University of Post and Telecommunications,
More informationImage retrieval based on bag of images
University of Wollongong Research Online Faculty of Informatics - Papers (Archive) Faculty of Engineering and Information Sciences 2009 Image retrieval based on bag of images Jun Zhang University of Wollongong
More informationColor Image Segmentation Using a Spatial K-Means Clustering Algorithm
Color Image Segmentation Using a Spatial K-Means Clustering Algorithm Dana Elena Ilea and Paul F. Whelan Vision Systems Group School of Electronic Engineering Dublin City University Dublin 9, Ireland danailea@eeng.dcu.ie
More informationThe goals of segmentation
Image segmentation The goals of segmentation Group together similar-looking pixels for efficiency of further processing Bottom-up process Unsupervised superpixels X. Ren and J. Malik. Learning a classification
More informationContent Based Image Retrieval Using Color Quantizes, EDBTC and LBP Features
Content Based Image Retrieval Using Color Quantizes, EDBTC and LBP Features 1 Kum Sharanamma, 2 Krishnapriya Sharma 1,2 SIR MVIT Abstract- To describe the image features the Local binary pattern (LBP)
More informationFace Recognition Using Vector Quantization Histogram and Support Vector Machine Classifier Rong-sheng LI, Fei-fei LEE *, Yan YAN and Qiu CHEN
2016 International Conference on Artificial Intelligence: Techniques and Applications (AITA 2016) ISBN: 978-1-60595-389-2 Face Recognition Using Vector Quantization Histogram and Support Vector Machine
More informationSize Regularized Cut for Data Clustering
Size Regularized Cut for Data Clustering Yixin Chen Department of CS Univ. of New Orleans yixin@cs.uno.edu Ya Zhang Department of EECS Uinv. of Kansas yazhang@ittc.ku.edu Xiang Ji NEC-Labs America, Inc.
More informationImproving the Efficiency of Fast Using Semantic Similarity Algorithm
International Journal of Scientific and Research Publications, Volume 4, Issue 1, January 2014 1 Improving the Efficiency of Fast Using Semantic Similarity Algorithm D.KARTHIKA 1, S. DIVAKAR 2 Final year
More informationTargil 12 : Image Segmentation. Image segmentation. Why do we need it? Image segmentation
Targil : Image Segmentation Image segmentation Many slides from Steve Seitz Segment region of the image which: elongs to a single object. Looks uniform (gray levels, color ) Have the same attributes (texture
More informationAN EFFICIENT BINARIZATION TECHNIQUE FOR FINGERPRINT IMAGES S. B. SRIDEVI M.Tech., Department of ECE
AN EFFICIENT BINARIZATION TECHNIQUE FOR FINGERPRINT IMAGES S. B. SRIDEVI M.Tech., Department of ECE sbsridevi89@gmail.com 287 ABSTRACT Fingerprint identification is the most prominent method of biometric
More informationImage Segmentation continued Graph Based Methods
Image Segmentation continued Graph Based Methods Previously Images as graphs Fully-connected graph node (vertex) for every pixel link between every pair of pixels, p,q affinity weight w pq for each link
More informationHIGH RESOLUTION REMOTE SENSING IMAGE SEGMENTATION BASED ON GRAPH THEORY AND FRACTAL NET EVOLUTION APPROACH
HIGH RESOLUTION REMOTE SENSING IMAGE SEGMENTATION BASED ON GRAPH THEORY AND FRACTAL NET EVOLUTION APPROACH Yi Yang, Haitao Li, Yanshun Han, Haiyan Gu Key Laboratory of Geo-informatics of State Bureau of
More informationContent Based Image Retrieval (CBIR) Using Segmentation Process
Content Based Image Retrieval (CBIR) Using Segmentation Process R.Gnanaraja 1, B. Jagadishkumar 2, S.T. Premkumar 3, B. Sunil kumar 4 1, 2, 3, 4 PG Scholar, Department of Computer Science and Engineering,
More informationCellular Learning Automata-Based Color Image Segmentation using Adaptive Chains
Cellular Learning Automata-Based Color Image Segmentation using Adaptive Chains Ahmad Ali Abin, Mehran Fotouhi, Shohreh Kasaei, Senior Member, IEEE Sharif University of Technology, Tehran, Iran abin@ce.sharif.edu,
More informationClustering. Informal goal. General types of clustering. Applications: Clustering in information search and analysis. Example applications in search
Informal goal Clustering Given set of objects and measure of similarity between them, group similar objects together What mean by similar? What is good grouping? Computation time / quality tradeoff 1 2
More informationHOW USEFUL ARE COLOUR INVARIANTS FOR IMAGE RETRIEVAL?
HOW USEFUL ARE COLOUR INVARIANTS FOR IMAGE RETRIEVAL? Gerald Schaefer School of Computing and Technology Nottingham Trent University Nottingham, U.K. Gerald.Schaefer@ntu.ac.uk Abstract Keywords: The images
More informationAn Introduction to Content Based Image Retrieval
CHAPTER -1 An Introduction to Content Based Image Retrieval 1.1 Introduction With the advancement in internet and multimedia technologies, a huge amount of multimedia data in the form of audio, video and
More informationCS246: Mining Massive Datasets Jure Leskovec, Stanford University
CS246: Mining Massive Datasets Jure Leskovec, Stanford University http://cs246.stanford.edu HITS (Hypertext Induced Topic Selection) Is a measure of importance of pages or documents, similar to PageRank
More informationIMPROVING THE PERFORMANCE OF CONTENT-BASED IMAGE RETRIEVAL SYSTEMS WITH COLOR IMAGE PROCESSING TOOLS
IMPROVING THE PERFORMANCE OF CONTENT-BASED IMAGE RETRIEVAL SYSTEMS WITH COLOR IMAGE PROCESSING TOOLS Fabio Costa Advanced Technology & Strategy (CGISS) Motorola 8000 West Sunrise Blvd. Plantation, FL 33322
More information[2006] IEEE. Reprinted, with permission, from [Wenjing Jia, Huaifeng Zhang, Xiangjian He, and Qiang Wu, A Comparison on Histogram Based Image
[6] IEEE. Reprinted, with permission, from [Wenjing Jia, Huaifeng Zhang, Xiangjian He, and Qiang Wu, A Comparison on Histogram Based Image Matching Methods, Video and Signal Based Surveillance, 6. AVSS
More informationSupervised texture detection in images
Supervised texture detection in images Branislav Mičušík and Allan Hanbury Pattern Recognition and Image Processing Group, Institute of Computer Aided Automation, Vienna University of Technology Favoritenstraße
More informationMultiple Constraint Satisfaction by Belief Propagation: An Example Using Sudoku
Multiple Constraint Satisfaction by Belief Propagation: An Example Using Sudoku Todd K. Moon and Jacob H. Gunther Utah State University Abstract The popular Sudoku puzzle bears structural resemblance to
More informationA Robust Wipe Detection Algorithm
A Robust Wipe Detection Algorithm C. W. Ngo, T. C. Pong & R. T. Chin Department of Computer Science The Hong Kong University of Science & Technology Clear Water Bay, Kowloon, Hong Kong Email: fcwngo, tcpong,
More informationImage Segmentation continued Graph Based Methods. Some slides: courtesy of O. Capms, Penn State, J.Ponce and D. Fortsyth, Computer Vision Book
Image Segmentation continued Graph Based Methods Some slides: courtesy of O. Capms, Penn State, J.Ponce and D. Fortsyth, Computer Vision Book Previously Binary segmentation Segmentation by thresholding
More informationClustering and Visualisation of Data
Clustering and Visualisation of Data Hiroshi Shimodaira January-March 28 Cluster analysis aims to partition a data set into meaningful or useful groups, based on distances between data points. In some
More informationImage Segmentation. Ross Whitaker SCI Institute, School of Computing University of Utah
Image Segmentation Ross Whitaker SCI Institute, School of Computing University of Utah What is Segmentation? Partitioning images/volumes into meaningful pieces Partitioning problem Labels Isolating a specific
More informationCS246: Mining Massive Datasets Jure Leskovec, Stanford University
CS246: Mining Massive Datasets Jure Leskovec, Stanford University http://cs246.stanford.edu SPAM FARMING 2/11/2013 Jure Leskovec, Stanford C246: Mining Massive Datasets 2 2/11/2013 Jure Leskovec, Stanford
More informationNormalized cuts and image segmentation
Normalized cuts and image segmentation Department of EE University of Washington Yeping Su Xiaodan Song Normalized Cuts and Image Segmentation, IEEE Trans. PAMI, August 2000 5/20/2003 1 Outline 1. Image
More informationHierarchical Multi level Approach to graph clustering
Hierarchical Multi level Approach to graph clustering by: Neda Shahidi neda@cs.utexas.edu Cesar mantilla, cesar.mantilla@mail.utexas.edu Advisor: Dr. Inderjit Dhillon Introduction Data sets can be presented
More informationComputer Vision 5 Segmentation by Clustering
Computer Vision 5 Segmentation by Clustering MAP-I Doctoral Programme Miguel Tavares Coimbra Outline Introduction Applications Simple clustering K-means clustering Graph-theoretic clustering Acknowledgements:
More informationUsing the Kolmogorov-Smirnov Test for Image Segmentation
Using the Kolmogorov-Smirnov Test for Image Segmentation Yong Jae Lee CS395T Computational Statistics Final Project Report May 6th, 2009 I. INTRODUCTION Image segmentation is a fundamental task in computer
More informationMaximizing edge-ratio is NP-complete
Maximizing edge-ratio is NP-complete Steven D Noble, Pierre Hansen and Nenad Mladenović February 7, 01 Abstract Given a graph G and a bipartition of its vertices, the edge-ratio is the minimum for both
More informationA Patent Retrieval Method Using a Hierarchy of Clusters at TUT
A Patent Retrieval Method Using a Hierarchy of Clusters at TUT Hironori Doi Yohei Seki Masaki Aono Toyohashi University of Technology 1-1 Hibarigaoka, Tenpaku-cho, Toyohashi-shi, Aichi 441-8580, Japan
More informationTexture. Texture is a description of the spatial arrangement of color or intensities in an image or a selected region of an image.
Texture Texture is a description of the spatial arrangement of color or intensities in an image or a selected region of an image. Structural approach: a set of texels in some regular or repeated pattern
More informationSTUDYING THE FEASIBILITY AND IMPORTANCE OF GRAPH-BASED IMAGE SEGMENTATION TECHNIQUES
25-29 JATIT. All rights reserved. STUDYING THE FEASIBILITY AND IMPORTANCE OF GRAPH-BASED IMAGE SEGMENTATION TECHNIQUES DR.S.V.KASMIR RAJA, 2 A.SHAIK ABDUL KHADIR, 3 DR.S.S.RIAZ AHAMED. Dean (Research),
More information6.801/866. Segmentation and Line Fitting. T. Darrell
6.801/866 Segmentation and Line Fitting T. Darrell Segmentation and Line Fitting Gestalt grouping Background subtraction K-Means Graph cuts Hough transform Iterative fitting (Next time: Probabilistic segmentation)
More informationInternational Journal of Advanced Research in Computer Science and Software Engineering
Volume 2, Issue 9, September 2012 ISSN: 2277 128X International Journal of Advanced Research in Computer Science and Software Engineering Research Paper Available online at: www.ijarcsse.com A New Method
More informationA Modified Approach for Image Segmentation in Information Bottleneck Method
A Modified Approach for Image Segmentation in Information Bottleneck Method S.Dhanalakshmi 1 and Dr.T.Ravichandran 2 Associate Professor, Department of Computer Science & Engineering, SNS College of Technology,Coimbatore-641
More informationGraph Matching: Fast Candidate Elimination Using Machine Learning Techniques
Graph Matching: Fast Candidate Elimination Using Machine Learning Techniques M. Lazarescu 1,2, H. Bunke 1, and S. Venkatesh 2 1 Computer Science Department, University of Bern, Switzerland 2 School of
More informationBig Data Analytics. Special Topics for Computer Science CSE CSE Feb 11
Big Data Analytics Special Topics for Computer Science CSE 4095-001 CSE 5095-005 Feb 11 Fei Wang Associate Professor Department of Computer Science and Engineering fei_wang@uconn.edu Clustering II Spectral
More informationContent based Image Retrieval Using Multichannel Feature Extraction Techniques
ISSN 2395-1621 Content based Image Retrieval Using Multichannel Feature Extraction Techniques #1 Pooja P. Patil1, #2 Prof. B.H. Thombare 1 patilpoojapandit@gmail.com #1 M.E. Student, Computer Engineering
More informationClustering Algorithms for general similarity measures
Types of general clustering methods Clustering Algorithms for general similarity measures general similarity measure: specified by object X object similarity matrix 1 constructive algorithms agglomerative
More informationImage Segmentation. Ross Whitaker SCI Institute, School of Computing University of Utah
Image Segmentation Ross Whitaker SCI Institute, School of Computing University of Utah What is Segmentation? Partitioning images/volumes into meaningful pieces Partitioning problem Labels Isolating a specific
More informationImage Analysis - Lecture 5
Texture Segmentation Clustering Review Image Analysis - Lecture 5 Texture and Segmentation Magnus Oskarsson Lecture 5 Texture Segmentation Clustering Review Contents Texture Textons Filter Banks Gabor
More informationhttp://www.xkcd.com/233/ Text Clustering David Kauchak cs160 Fall 2009 adapted from: http://www.stanford.edu/class/cs276/handouts/lecture17-clustering.ppt Administrative 2 nd status reports Paper review
More informationSegmentation Computer Vision Spring 2018, Lecture 27
Segmentation http://www.cs.cmu.edu/~16385/ 16-385 Computer Vision Spring 218, Lecture 27 Course announcements Homework 7 is due on Sunday 6 th. - Any questions about homework 7? - How many of you have
More informationSegmentation. Bottom Up Segmentation
Segmentation Bottom up Segmentation Semantic Segmentation Bottom Up Segmentation 1 Segmentation as clustering Depending on what we choose as the feature space, we can group pixels in different ways. Grouping
More informationCorrelation Based Feature Selection with Irrelevant Feature Removal
Available Online at www.ijcsmc.com International Journal of Computer Science and Mobile Computing A Monthly Journal of Computer Science and Information Technology IJCSMC, Vol. 3, Issue. 4, April 2014,
More informationSemi-Automatic Transcription Tool for Ancient Manuscripts
The Venice Atlas A Digital Humanities atlas project by DH101 EPFL Students Semi-Automatic Transcription Tool for Ancient Manuscripts In this article, we investigate various techniques from the fields of
More informationColor Image Segmentation
Color Image Segmentation Yining Deng, B. S. Manjunath and Hyundoo Shin* Department of Electrical and Computer Engineering University of California, Santa Barbara, CA 93106-9560 *Samsung Electronics Inc.
More informationData Mining. Part 2. Data Understanding and Preparation. 2.4 Data Transformation. Spring Instructor: Dr. Masoud Yaghini. Data Transformation
Data Mining Part 2. Data Understanding and Preparation 2.4 Spring 2010 Instructor: Dr. Masoud Yaghini Outline Introduction Normalization Attribute Construction Aggregation Attribute Subset Selection Discretization
More informationA Graph Clustering Algorithm Based on Minimum and Normalized Cut
A Graph Clustering Algorithm Based on Minimum and Normalized Cut Jiabing Wang 1, Hong Peng 1, Jingsong Hu 1, and Chuangxin Yang 1, 1 School of Computer Science and Engineering, South China University of
More informationPattern Recognition Lecture Sequential Clustering
Pattern Recognition Lecture Prof. Dr. Marcin Grzegorzek Research Group for Pattern Recognition Institute for Vision and Graphics University of Siegen, Germany Pattern Recognition Chain patterns sensor
More informationIJREAT International Journal of Research in Engineering & Advanced Technology, Volume 1, Issue 5, Oct-Nov, 2013 ISSN:
Semi Automatic Annotation Exploitation Similarity of Pics in i Personal Photo Albums P. Subashree Kasi Thangam 1 and R. Rosy Angel 2 1 Assistant Professor, Department of Computer Science Engineering College,
More informationEfficient Acquisition of Human Existence Priors from Motion Trajectories
Efficient Acquisition of Human Existence Priors from Motion Trajectories Hitoshi Habe Hidehito Nakagawa Masatsugu Kidode Graduate School of Information Science, Nara Institute of Science and Technology
More informationBehavioral Data Mining. Lecture 18 Clustering
Behavioral Data Mining Lecture 18 Clustering Outline Why? Cluster quality K-means Spectral clustering Generative Models Rationale Given a set {X i } for i = 1,,n, a clustering is a partition of the X i
More informationTexture Segmentation by Windowed Projection
Texture Segmentation by Windowed Projection 1, 2 Fan-Chen Tseng, 2 Ching-Chi Hsu, 2 Chiou-Shann Fuh 1 Department of Electronic Engineering National I-Lan Institute of Technology e-mail : fctseng@ccmail.ilantech.edu.tw
More informationLearning to Match. Jun Xu, Zhengdong Lu, Tianqi Chen, Hang Li
Learning to Match Jun Xu, Zhengdong Lu, Tianqi Chen, Hang Li 1. Introduction The main tasks in many applications can be formalized as matching between heterogeneous objects, including search, recommendation,
More informationAnalysis of Image and Video Using Color, Texture and Shape Features for Object Identification
IOSR Journal of Computer Engineering (IOSR-JCE) e-issn: 2278-0661,p-ISSN: 2278-8727, Volume 16, Issue 6, Ver. VI (Nov Dec. 2014), PP 29-33 Analysis of Image and Video Using Color, Texture and Shape Features
More informationA Fast Distance Between Histograms
Fast Distance Between Histograms Francesc Serratosa 1 and lberto Sanfeliu 2 1 Universitat Rovira I Virgili, Dept. d Enginyeria Informàtica i Matemàtiques, Spain francesc.serratosa@.urv.net 2 Universitat
More informationHistogram and watershed based segmentation of color images
Histogram and watershed based segmentation of color images O. Lezoray H. Cardot LUSAC EA 2607 IUT Saint-Lô, 120 rue de l'exode, 50000 Saint-Lô, FRANCE Abstract A novel method for color image segmentation
More informationProblem Definition. Clustering nonlinearly separable data:
Outlines Weighted Graph Cuts without Eigenvectors: A Multilevel Approach (PAMI 2007) User-Guided Large Attributed Graph Clustering with Multiple Sparse Annotations (PAKDD 2016) Problem Definition Clustering
More informationBeyond Bags of Features
: for Recognizing Natural Scene Categories Matching and Modeling Seminar Instructed by Prof. Haim J. Wolfson School of Computer Science Tel Aviv University December 9 th, 2015
More informationCluster Analysis. Mu-Chun Su. Department of Computer Science and Information Engineering National Central University 2003/3/11 1
Cluster Analysis Mu-Chun Su Department of Computer Science and Information Engineering National Central University 2003/3/11 1 Introduction Cluster analysis is the formal study of algorithms and methods
More informationThe Bounded Edge Coloring Problem and Offline Crossbar Scheduling
The Bounded Edge Coloring Problem and Offline Crossbar Scheduling Jonathan Turner WUCSE-05-07 Abstract This paper introduces a variant of the classical edge coloring problem in graphs that can be applied
More informationRobotics Programming Laboratory
Chair of Software Engineering Robotics Programming Laboratory Bertrand Meyer Jiwon Shin Lecture 8: Robot Perception Perception http://pascallin.ecs.soton.ac.uk/challenges/voc/databases.html#caltech car
More informationTypes of image feature and segmentation
COMP3204/COMP6223: Computer Vision Types of image feature and segmentation Jonathon Hare jsh2@ecs.soton.ac.uk Image Feature Morphology Recap: Feature Extractors image goes in Feature Extractor featurevector(s)
More informationIntelligent management of on-line video learning resources supported by Web-mining technology based on the practical application of VOD
World Transactions on Engineering and Technology Education Vol.13, No.3, 2015 2015 WIETE Intelligent management of on-line video learning resources supported by Web-mining technology based on the practical
More informationShape Descriptor using Polar Plot for Shape Recognition.
Shape Descriptor using Polar Plot for Shape Recognition. Brijesh Pillai ECE Graduate Student, Clemson University bpillai@clemson.edu Abstract : This paper presents my work on computing shape models that
More informationPattern Mining. Knowledge Discovery and Data Mining 1. Roman Kern KTI, TU Graz. Roman Kern (KTI, TU Graz) Pattern Mining / 42
Pattern Mining Knowledge Discovery and Data Mining 1 Roman Kern KTI, TU Graz 2016-01-14 Roman Kern (KTI, TU Graz) Pattern Mining 2016-01-14 1 / 42 Outline 1 Introduction 2 Apriori Algorithm 3 FP-Growth
More informationA Systematic Overview of Data Mining Algorithms. Sargur Srihari University at Buffalo The State University of New York
A Systematic Overview of Data Mining Algorithms Sargur Srihari University at Buffalo The State University of New York 1 Topics Data Mining Algorithm Definition Example of CART Classification Iris, Wine
More informationCustomer Clustering using RFM analysis
Customer Clustering using RFM analysis VASILIS AGGELIS WINBANK PIRAEUS BANK Athens GREECE AggelisV@winbank.gr DIMITRIS CHRISTODOULAKIS Computer Engineering and Informatics Department University of Patras
More informationRepresentation of Quad Tree Decomposition and Various Segmentation Algorithms in Information Bottleneck Method
Representation of Quad Tree Decomposition and Various Segmentation Algorithms in Information Bottleneck Method S.Dhanalakshmi Professor,Department of Computer Science and Engineering, Malla Reddy Engineering
More informationCS 664 Slides #11 Image Segmentation. Prof. Dan Huttenlocher Fall 2003
CS 664 Slides #11 Image Segmentation Prof. Dan Huttenlocher Fall 2003 Image Segmentation Find regions of image that are coherent Dual of edge detection Regions vs. boundaries Related to clustering problems
More informationClustering. SC4/SM4 Data Mining and Machine Learning, Hilary Term 2017 Dino Sejdinovic
Clustering SC4/SM4 Data Mining and Machine Learning, Hilary Term 2017 Dino Sejdinovic Clustering is one of the fundamental and ubiquitous tasks in exploratory data analysis a first intuition about the
More informationMulti-scale Techniques for Document Page Segmentation
Multi-scale Techniques for Document Page Segmentation Zhixin Shi and Venu Govindaraju Center of Excellence for Document Analysis and Recognition (CEDAR), State University of New York at Buffalo, Amherst
More informationRank Measures for Ordering
Rank Measures for Ordering Jin Huang and Charles X. Ling Department of Computer Science The University of Western Ontario London, Ontario, Canada N6A 5B7 email: fjhuang33, clingg@csd.uwo.ca Abstract. Many
More informationCombinatorial optimization and its applications in image Processing. Filip Malmberg
Combinatorial optimization and its applications in image Processing Filip Malmberg Part 1: Optimization in image processing Optimization in image processing Many image processing problems can be formulated
More informationInteractive segmentation, Combinatorial optimization. Filip Malmberg
Interactive segmentation, Combinatorial optimization Filip Malmberg But first... Implementing graph-based algorithms Even if we have formulated an algorithm on a general graphs, we do not neccesarily have
More informationIntroduction to Medical Imaging (5XSA0) Module 5
Introduction to Medical Imaging (5XSA0) Module 5 Segmentation Jungong Han, Dirk Farin, Sveta Zinger ( s.zinger@tue.nl ) 1 Outline Introduction Color Segmentation region-growing region-merging watershed
More informationELEC Dr Reji Mathew Electrical Engineering UNSW
ELEC 4622 Dr Reji Mathew Electrical Engineering UNSW Review of Motion Modelling and Estimation Introduction to Motion Modelling & Estimation Forward Motion Backward Motion Block Motion Estimation Motion
More informationTRANSPARENT OBJECT DETECTION USING REGIONS WITH CONVOLUTIONAL NEURAL NETWORK
TRANSPARENT OBJECT DETECTION USING REGIONS WITH CONVOLUTIONAL NEURAL NETWORK 1 Po-Jen Lai ( 賴柏任 ), 2 Chiou-Shann Fuh ( 傅楸善 ) 1 Dept. of Electrical Engineering, National Taiwan University, Taiwan 2 Dept.
More informationA Review: Content Base Image Mining Technique for Image Retrieval Using Hybrid Clustering
A Review: Content Base Image Mining Technique for Image Retrieval Using Hybrid Clustering Gurpreet Kaur M-Tech Student, Department of Computer Engineering, Yadawindra College of Engineering, Talwandi Sabo,
More informationCommunity Structure Detection. Amar Chandole Ameya Kabre Atishay Aggarwal
Community Structure Detection Amar Chandole Ameya Kabre Atishay Aggarwal What is a network? Group or system of interconnected people or things Ways to represent a network: Matrices Sets Sequences Time
More informationLecture 11: E-M and MeanShift. CAP 5415 Fall 2007
Lecture 11: E-M and MeanShift CAP 5415 Fall 2007 Review on Segmentation by Clustering Each Pixel Data Vector Example (From Comanciu and Meer) Review of k-means Let's find three clusters in this data These
More informationData Clustering Hierarchical Clustering, Density based clustering Grid based clustering
Data Clustering Hierarchical Clustering, Density based clustering Grid based clustering Team 2 Prof. Anita Wasilewska CSE 634 Data Mining All Sources Used for the Presentation Olson CF. Parallel algorithms
More informationElimination of Duplicate Videos in Video Sharing Sites
Elimination of Duplicate Videos in Video Sharing Sites Narendra Kumar S, Murugan S, Krishnaveni R Abstract - In some social video networking sites such as YouTube, there exists large numbers of duplicate
More informationLecture 1: Introduction and Motivation Markus Kr otzsch Knowledge-Based Systems
KNOWLEDGE GRAPHS Introduction and Organisation Lecture 1: Introduction and Motivation Markus Kro tzsch Knowledge-Based Systems TU Dresden, 16th Oct 2018 Markus Krötzsch, 16th Oct 2018 Course Tutors Knowledge
More information