AN IMPACT OF HISTOGRAM OF ORIENTED GRADIENTS AND DISTANCE MEASURES IN IMAGE RETRIEVAL

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1 AN IMPACT OF HISTOGRAM OF ORIENTED GRADIENTS AND DISTANCE MEASURES IN IMAGE RETRIEVAL Anirudh N Kashyap 1, Anvitha S 2, Malvika Mohan 3, Kishan M V 4 1,2,3,4 Department of Electronics & Communication Engineering, S. J. B Institute of Technology Bengaluru, India ABSTRACT: Object taxonomy is a challenging task in computer vision and pattern recognition applications where Content-Based Image Retrieval (CBIR) is an effective approach. Content-based image retrieval uses the visual contents of an image to represent and to index the image. A CBIR system acquires useful information from the image contents for retrieving required image and guides the user to extract database images relevant to the contents of a query image. In this project, an automatic discovery of regularities in data is presented and with the use of these regularities, actions are taken to classify the image data into different categories. Various transformation techniques and different classification methods are used in order to obtain better correctness rate. The obtained features from transformation techniques are given as input to classification methods. The proposed method includes HOG, through which feature vectors are computed. Thus, the extracted feature vectors which are projected onto reduced dimensional feature space using PCA to increase numerical stability. Further, taxonomy is performed using classifiers like similarity/distance measures. Keywords: Image Retrieval, Low Level Features, High level Semantics, HOG, PCA, Distance Measures. [1] INTRODUCTION Anirudh N Kashyap 1, Anvitha S 2, Malvika Mohan 3, Kishan M V 4 199

2 AN IMPACT OF HISTOGRAM OF ORIENTED GRADIENTS AND DISTANCE MEASURES IN IMAGE RETRIEVAL The recent progress in digital technology has led to the increase in the number of digital images and digital image databases. This has created the need for an image retrieval system which not only effective but also efficient. In recent years, image retrieval has seen progress in so many ways to satisfy needs of human perception. The most relevant approaches deals with 100 or more types of categories which can be found in certain standard and popular databases such as Caltech-101 and Caltech-256. Even after so much advancement, designers still find it very difficult to design an algorithm which retrieves appropriate images. Content Based Image Retrieval is the most sort after method to categorize images based on visual contents like color, texture and shape [1]. Color features represent the chromatic part of an image thereby providing the measure of similitude in colors. In texture features the disparities present in brightness with respect to high frequencies are looked into. Shape features are segregated into global form and local elements. These image features are further categorized into two categories that are template based and histogram based. Though template based methods produce good results in detection of objects belonging to a particular category, it shows no deviation with respect to object transforms. A better approach to this problem is histogram based deviation. [2] PROPOSED METHODOLOGY: The proposed technique describes a method where the images are compared to images already stored in databases. In the training phase, the images are first resized. Then the HOG features are extracted where intensity gradients are computed by max pooling technique on edge directions. The extracted feature vectors are extrapolated onto a compressed dimensional feature space using PCA. The above mentioned steps are explained briefly below: In the past, many methods of image extraction have been devised. Yet they are not completely useful for image classification for multiple classes of images. Hence we use Histogram of Oriented Gradients, which is similar to how humans process an object based on shape and 200

3 edge orientation. The basic principle behind HOG is that object perception and its shape within an image can be described by a plot of intensity gradients or edge directions. This can be implemented by segregating the images into smaller portions called cells and then computing the histogram of gradients. As the method is been inspired by [4] where the higher Area under ROC Curve (AUC) was achieved with 9 rectangular cells and 9 bin histogram per cell. We consider these nine histograms with nine bins that are concatenated to obtain 81 significant features. The two steps involved in computing Histogram of Oriented Gradients is explained below: [2.1] HISTOGRAM OF ORIENTED GRADIENTS This first step involves the computation of gradient values. The gradient values are obtained by applying 1-D centered, discrete derivative in one or both of the horizontal and vertical directions. Filters are used to obtain the edge gradients and orientations [13]. The gradient magnitude m(u,v) is calculated using u and v directional gradients du(u, v) and dv(u, v) respectively as given in following equation: m(u,v) =duu,v +dvu,v (1) The second step involves computation of orientation by creating the cell histograms based on the values found in the gradient computation by max pooling technique where the histogram channels are spread over 0 to 180 degrees or 0 to 360 degrees, depending on whether the gradient is unsigned or signed. The orientation φ(u,v) is calculated as follows: tan,,if duu,v <0 & dvu,v<0, φu,v = tan, +,if duu,v <0 & dvu,v<0, tan,, otherwise (2) The proposed method involves training a set of images into a system which serves as a dataset. The testing image is input into the system. HOG is applied to both the testing as well as training phase and the feature vector is computed using the above formulas. These features of the trained vector undergo dimensionality reduction through the process of PCA. [2.2]PRINCIPLE COMPONENT ANALYSIS This is an effective method which along with HOG can be utilized to obtain features from respectable regions. It is a mathematical method which converts a number of correlated data into fewer uncorrelated data known as principle components. As mentioned above, the featured vectors extracted during HOG are given to PCA for conversion to newer variables. This process is performed to preserve decomposition of a data covariance matrix or singular value decomposition of a data matrix. The following are the steps involved for the PCA:- Anirudh N Kashyap 1, Anvitha S 2, Malvika Mohan 3, Kishan M V 4 201

4 AN IMPACT OF HISTOGRAM OF ORIENTED GRADIENTS AND DISTANCE MEASURES IN IMAGE RETRIEVAL Consider a 2D vector of size (M x M) that is represented as 1D vector by transforming each row into a long vertical vector. Let N be the number of vectors of size K(M x M), p j be the pixel value and j= 1..N, N=81. 1D image vector, Y j =[ p 1...p L] T (3) The images are mean centered by subtracting the mean image from each image vector. Let n represent the mean image of order K X 1. Mean image. * n = 1/81 y +, j (4) The mean centered image V j = y j- n (5) And the covariance matrix D is calculated as: D =1/ 81 j v j T (6) The Eigen vectors e j and the corresponding Eigen values λ j, are calculated. From the above N Eigen vectors, only l vectors of largest Eigen values are considered as Eigen vectors which represent the deterministic feature of an image. Using the l Eigen vector e l and j=1, 2 81, features are extracted as: G j=e lt (y j-n) (7) Where G j is the feature vector of image j. After projecting a new image into the Eigen space we get its feature vector G j new. Now we calculate the distances between new image and each known image using classification techniques like distance measures. Similarity Distance Measures: In the proposed work, various distance measures are used for object classification. Let Y{y1, y2,..., ym} and X {x1, x2,..., xm} be feature vectors of length n, then the distances between feature vectors of test and train images are calculated based on their statistical expressions. In this regard, we consider different distance measure techniques such as Manhattan distance, Euclidean distance, Modified Squared Euclidean distance and Angle based distance to acquire an average classification rate. [2.3]ALGORITHM A. Training phase: Input: Output: A set of randomly selected training sample images. A knowledge database (KDB). Method: 1. Attain training samples 2. Feature Extraction using HOG: i. Read the images from large datasets ii. Convert the above color images into gray scale images and resize the image iii. Apply HOG on these images to obtain feature vectors iv. The obtained values are reduced in dimension using PCA 202

5 3. Apply PCA for dimensionality trimming. 4. Storage of feature matrix in Knowledge Database. B. Testing Phase: Input: 1. A Knowledge Database(KDB) 2. Query image I. Output: Class label/ Identity of I. Method: 1. Obtain the feature matrix of the pattern I 2. Apply similarity measure for successive classifications 3. Label the class/identity using step 2. [5] EXPERIMENTAL REULTS & PERFORMANCE ANALYSIS Below are the results of comparison of our methodology against other methods on some of the standard and popular databases. COREL-1K TABLE I PERFORMANCE ANALYSIS OF COREL-1K Method 15 Training images/cat 30 Training images/cat Thanwari et al[7] - 53 Wang et al.[8 ] - 72 DCT+DM[12] DCT+NN[12] DWT+DM[12] DWT+NN[12] Ridgelet+ DM[11] Ridgelet+NN[11] HOG+DM Caltech 101 Method TABLE II PERFORMANCE ANALYSIS OF CALTECH Training images/cat 30 Training images/cat Fei-Fei et al[3] Serre et. al[9] Holub et. al[4] Mahantesh et. al[10] DCT+DM[12] Anirudh N Kashyap 1, Anvitha S 2, Malvika Mohan 3, Kishan M V 4 203

6 AN IMPACT OF HISTOGRAM OF ORIENTED GRADIENTS AND DISTANCE MEASURES IN IMAGE RETRIEVAL DCT+NN[12] DWT+DM[12] DWT+NN[12] Ridgelet + DM[11] Ridgelet +NN[11] HOG+DM Caltech-256 TABLE III PERFORMANCE ANALYSIS OF CALTECH- 256 Method 15 Training images/cat 30 Training images/cat Griffin et al[5] Yang et. al[6] Mahantesh et. al[10] DCT+DM[12] DCT+NN[12] DWT+DM[12] DWT+NN[12] Ridgele t+ DM[11] Ridgele t+ NN[11] HOG+DM [6] CONCLUSION In this project, HOG technique is used for feature extraction and the distance measure technique is used for classification. The HOG technique extracts about 8100 coefficients. These coefficients are also further reduced using PCA. Principle component analysis based methods are popular for studying high dimensional data. This analysis involves properties such as linearity, largest variances, mean and orthogonality. Although PCA has its limitations, it is often a useful first step in re-describing data in terms of its hidden structures. The reduction of image data dimensions makes the analysis simpler and smaller without any loss of useful information. There are a variety of feature extraction and classification techniques present, but feature extraction using HOG with dimensionality reduction using PCA has gained lot of importance in classifying different categories in pattern recognition applications. Therefore, an effective approach of descriptor technique in PCA transformed space is brought to generate compact discriminative features to increase the performance of image retrieval systems. Hence, Histogram of Orientated Gradients is used as it is similar to Human visual processing system which mainly concentrates on local shape and edge information of an object with the distribution of intensity gradients. 204

7 REFERENCES [1] K. Mahantesh, V.N. Manjunath Aradhya, S.K. Niranjan An impact of complex hybrid color space in image segmentation. International Symposium on Intelligent Informatics, Springer, vol.235, pp.73-82, [2] Fazal Malik, Baharum Baharudin. An analysis of distance metrics in content-based image retrieval using statistical quantized histogram texture features in the DCT domain. vol.2, no.25, pp , 2013 [3].L. Fei-Fei, R. Fergus, P. Perona An incremental bayesian approach testing on 101 object categories. Workshop on Generative-Model Based Vision, CVPR, [4] A.Holub, M. Welling, P. Perona Exploiting unlabelled data for hybrid object classification. National Information Processing System Workshop on Inter-Class Transfer,2005. [5] G. Griffin, A. Holub, P.Perona. Caltech-256 object category dataset.vol.7694, 2007 [6] J. Yang, K. Yu, Y. Gong, T. Huang. Linear spatial pyramid matching using sparse coding for image classification. Proceedings of the International Conference on Computer Vision and Pattern Recognition, IEEE.pp , [7] P.B. Thanwari and N.J. Janwe. Cbir based on color and texture. International Journal of Information Technology and Knowledge Management, 4:99 132, , 4.1, 4.2 [8] J.Wang, J.Yang, T. Huang, and Y.Gong. Locality constraint linear coding for image classification. Computer Vision and Pattern Recognition, pages , , 4.2, 4.1 Anirudh N Kashyap 1, Anvitha S 2, Malvika Mohan 3, Kishan M V 4 205

8 AN IMPACT OF HISTOGRAM OF ORIENTED GRADIENTS AND DISTANCE MEASURES IN IMAGE RETRIEVAL [9] [10] T. Serre, L. Wolf, and T. Poggio. Object recognition with features inspired by visual cortex. Computer Vision and Pattern Recognition, 2:994, , 3.2.4,4.3, 4.1, 4.4 [11] K. Mahantesh and V.N. Manjunath Aradhya. An exploration of ridgelet transform to handle higher dimensional intermittency for object categorization in large image datasets. Elsevier Publications, ICAICT. 2, 3.2.3, 4.3, 4.1, 4.4, 4.5,4.6 [12] Belavadi Bhaskar, K. Mahantesh, G.P. Geetha, An investigation of fsvd and Ridgelet Transform For Illumination And Expression Invariant Face Recognition. Springer International Publishing, Switzerland 2015 [13] K. Mahantesh, V.N Aradhya and S.K Niranjan, Coslets:A Novel Approach To Explore Object Taxonomy In Compressed DCT Domain For Large Image Datasets. Springer International Publishing, Switzerland [14] Takuya Kobayashi, Akinori Hidaka, Takio Kurita. Selection of Histograms of Oriented Gradients Features for Pedestrian Detection.Part II, LNCS 4985, pp , [15] J. Yang, Y. Gong K. Yu, and T. Huang. Linear spatial pyramid matching using sparse coding for image classification. Proceedings of the International Conference on Computer Vision and Pattern Recognition,IEEE. 2, 4.5, 4.6, 4.1,5 Corresponding Address- 1) Anirudh N Kashyap #2629, 1 st floor, 5 th main, 2 nd stage Kumaraswamy layout, Bangalore Contact: (anirudhnk108@gmail.com) 2) Anvitha Sathish, #5, 240/7, BEML 5 th stage Rajarajeshwari Nagar, Bangalore Contact: (anvithasathish@gmail.com) 3) Malvika Mohan #3B, Rajeshwari Dwellings,1st cross, Ideal Homes township Rajarajeshwari Nagar, Bangalore Contact: (malvikamohan18@gmail.com) 4) Kishan M V #1371/A, 3rd Main, 6th Cross, 206

9 Girinagar Nagar, Bangalore , Contact Details: Anirudh N Kashyap 1, Anvitha S 2, Malvika Mohan 3, Kishan M V 4 207