2. LITERATURE REVIEW

Transcription

1 2. LITERATURE REVIEW CBIR has come long way before 1990 and very little papers have been published at that time, however the number of papers published since 1997 is increasing. There are many CBIR algorithms as the result of those researches and most of those algorithms process image into several layers of tasks. Those layers of tasks consist of extracting the multidimensional features of an image query and compare it with images in the database are perform after the system populate database with images. Populating database with extracted information from the images and indexed appropriately will affect the performance of retrieval. The information consists of color, shape, texture and the rest of image characteristic [1]. Features that most method focuses on are color, shape and texture. For color, a significant improvement over the RGB-color space use of opponent color representation uses the opponent color axes (R-G, 2B-R-G, R+G+B) [2] is one way to represent color of an image. There is also a method called Color Predominance Method which scans the image and replaces each pixel color with the new RGB color list [3, 4], gave an example indexing using texture where an image is indexed by a vector (w1, w2, w3, w4, w5, w6) representing the estimated proportion of texture where wt is the proportion of pixels classified with texture they are introducing indexing using Intermediate Features [5]. The number of digital medical images is rapidly rising, prompting the need for improved storage and retrieval systems. Most retrieval in these systems are based on the patient identification information or image modality as it is defined in the DICOM standard [6], but it is hoped that inclusion of other features can improve the effectiveness of this type of system. Archimedes includes retrieval based on features and combinations of features, as well as on patient identification information, doctor s notations, and image modality in order to develop effective CBIR. Archimedes system design enables researchers to upload their feature sets and quickly compare the effectiveness of their methods against other stored feature sets [7]. Searching using a combination of more than one image feature for example region and color improves retrieval effectiveness. Using a single-region query example is better than using the whole image as the query example. However, the multiple-region query examples outperformed the single-region query example and also the whole-image example queries [8]. 4

2 The Gabor filter has been widely used to extract image features, especially texture features. It is optimal in terms of minimizing the joint uncertainty in space and frequency, and is often used as an orientation and scale tunable edge and line (bar) detector. There have been many approaches proposed to characterize textures of images based on Gabor filters. In Gabor methods, a particular set of Gabor filters (corresponding to different angles) is chosen, which determines the quality of result in applications such as CBIR. To get rid of the angle dependence, some types of permutations on feature matrices are taken in. In the traditional application of Gabor filters the chosen directions may not correspond to the orientation of the contents in the query image. Therefore any method that extracts features independent of orientation in the image is desirable. Thus rotation invariance is particularly useful when one wants to retrieve images having same content but indifferent orientation. The modified Gabor function suitably in such a way that the resulting function besides inheriting good properties of Gabor filters is a Radial Basis function (RBF), which is an angle independent function. Hence no specific set of angles is required for feature extraction. The main features of the present algorithm are: (a) it uses images in Cartesian domain avoiding the nonlinear polar transformation, and certain approximations resulting there from, (b) it does not require, unlike standard Gabor method, direction dependent filters for the extraction of information pertaining to different directions, which minimizes the amount of computation. Additionally, our feature extraction procedure is independent of presence of rotation in images, and hence is useful for rotation independent CBIR [9]. One can assume that the goal of content based image retrieval is to find images which are both semantically and visually relevant to users based on image descriptors. These descriptors are often provided by an example image--the query by example paradigm. The CBIR system used in this work is an application of the system developed for modeling the joint probability of image region features and associated text. It is not necessary to train the model on both text and image data, and use two variants of the model one where both text and image data is used, and one where only image data is used. To evaluate CBIR systems a subject with query and corresponding result image pairs. The subject evaluates each pair as either undecided, poor match, faint match, or good match. Thus evaluating the query by image example paradigm [10]. Several methods for retrieving images on the basis of color similarity have been described in the literature, but most are variations on the same basic idea. Each image added to the collection is analyzed to compute a color histogram which shows the proportion of 5

3 pixels of each color within the image. The color histogram for each image is then stored in the database. The approach more frequently adopted for CBIR systems is based on the conventional color histogram (CCH), which contains occurrences of each color obtained counting all image pixels having that color. Each pixel is associated to a specific histogram bin only on the basis of its own color, and color similarity across different bins or color dissimilarity in the same bin are not taken into account. Since any pixel in the image can be described by three components in a certain color space (for instance, red, green and blue components in RGB space or hue, saturation and value in HSV space), a histogram, i.e. the distribution of the number of pixels for each quantized bin, can be defined for each component. Clearly, the more bins a color histogram contains the more discrimination power it has. However, a histogram with large number of bins will not only increase the computational cost, but will also be in appropriate for building efficient indexes for image data base. The conventional color histogram with quadratic form (QF) distance as similarity measure and the fuzzy color histogram with Euclidean Distance almost similar in their performance. But they couldn t respond well to shifted or translated images. In order to overcome this problem invariant color histogram technique is used makes which use of gradients in different channels that weight that weight the influence of a pixel on the histogram to cancel out the changes induced by deformations. When a rotated image is given as the query, the original image is retrieved as the closest match [11]. Color and Local Spatial Feature Histograms (CLSFH) has fewer feature indexes and can capture more color-spatial information in an image. At the same time, as the four histograms used by CLSFH are calculated globally on the image, the two local spatial statistic moment histograms and color histogram are insensitive to image rotation, translation and scaling, the local directional difference unit histogram is insensitive to image translation and scaling. In CLSFH, the non-uniform quantized HSV color model is used, the mean, the standard deviation of 5x5 neighbor of every pixel are calculated, and are used to generate the Local Mean Histogram, the Local Standard Deviation Histogram; the Directional Difference Unit of 3 X3 neighbor of every pixel is defined and computed, and is used to generate the Local Directional Difference Unit Histogram. The three histograms and color histogram are used as feature indexes to retrieve color image. So CLSFH is effective for images, especially for images with relatively regular texture and structure characteristic [12]. Content Based Image Retrieval (CBIR) systems based on shape using invariant image moments, like Moment Invariants (MI) and Zernike Moments (ZM) are available. MI and 6

4 ZM are good at representing the shape features of an image. However, non-orthogonality of MI and poor reconstruction of ZM restrict their application in CBIR. Faster and accurate CBIR algorithms are required for real time applications. This can be achieved by employing a classifier such as Support Vector Machine (SVM), SVM is a supervised learning method used for image classification. It views the given image database as two sets of vectors in an n dimensional space and constructs a separating hyper plane that maximizes the margin between the images relevant to query and the images non relevant to the query. A CBIR system using ELM features and ELM features with SVM as classifier [13]. The images are represented by a set of low-level features related to their structure and color distribution. Those descriptions are fed to a battery of image classifiers trained to evaluate the membership of the images with respect to a set of 14 overlapping classes. Packing together the scores vectors of prosemantic features are obtained, and used to index the images in an image retrieval system. The result show that the use of prosemantic features allows for a more successful and quick retrieval of the query images [14]. There are other fields in medicine which typically have large medical image archives. In particular, a huge amount of figures, graphs, images, and case examples is published in scientific literature, and the number of scientific journals that are published electronically is increasing. So to evaluate and estimate the impact of state-of-the-art medical CBIR integrated with text-based searches for retrieval of scientific literature. That is, investigate the use of bitmaps within the journal articles as additional information for retrieval, which reduces error rate of retrieval by 4.5% [15]. So far, automatic categorization of medical images is restricted to a small number of categories. A k-nearest-neighbor classifier (k-nn) is used, which embeds the distance measures. The categorization accuracy within a combined classifier, since their decision for each sample is less correlated with the decision made by the classifiers based on scaled representations. The classifier combination improves the results because the two single classifiers evaluate different aspects of an image [16]. If we compare the feature extraction techniques like Gabor, Wavelet and Histogram over color & texture features by considering three types of Distance Metric Measures like Euclidean Distance, Chi-Square Distance and Weighted Euclidean Distance, then from the quantitative measures the Gabor transform gives best result with more execution time compared to other techniques. Wavelet transform retrieve image at very fast rate compared to other techniques, but gives poor performance similarly color histogram also. But Gabor transform takes much time but gives the best results [17]. 7

5 A new approach for image indexing called wavelet correlogram is introduced by H. A. Moghaddam et al. According to this approach, wavelet coefficients are computed first to decompose space-frequency information of the image. These directional subbands enable us to compute the image spatial correlation in a more efficient way, while taking into consideration the semantic image information. A quantization step is applied before computing directional autocorrelograms of the wavelet coefficients and then index vectors are constructed using wavelet correlograms. The results obtained using above method are more effective and efficient compared to the indexing and retrieval methods based on wavelet transform [18]. T Celik and T. Tjahjadi proposes a multiscale texture classifier which uses features extracted from both magnitude and phase responses of subbands at different resolutions of the dual-tree complex wavelet transform decomposition of a texture image. The mean and entropy in the transform domain are used to form a feature vector. This technique can achieve a high texture classification rate even for small number of samples used in training stage. This method is suitable for applications where the number of texture samples used in training is very limited. The superior performance and robustness of this method is used for classifying and retrieving texture images from image databases [19]. S. Park et al propose a method of content-based image classification using a neural network. The images for classification are object images that can be divided into foreground and background. To deal with the object images efficiently, in the preprocessing step the object region is extracted using a region segmentation technique. Features for the classification are shape-based texture features extracted from wavelet-transformed images. The neural network classifier is constructed for the features using the back-propagation learning algorithm. Among the various texture features, the diagonal moment was the most effective. A test with 300 training data and 300 test data composed of 10 images from each of 30 classes shows classification rates of 81.7% and 76.7% correct. The classes with the feature values regularly distributed have higher classification rates [20]. Feature extraction is one of the most important tasks for efficient and accurate image retrieval purpose. Manesh Kokare et al presented a Cosine-modulated wavelet transform based technique for extraction of texture features. The major advantages of Cosine-modulated wavelet transform are less implementation complexity, good filter quality, and ease in imposing the regularity conditions. Texture features are obtained by computing the energy, standard deviation and their combination on each subband of the decomposed image. Retrieval efficiency and accuracy using Cosine-modulated wavelet based features is found to 8

6 be superior to other existing methods. Another advantage of proposed method is that the retrieval time required is 6.69 times less than the Gabor based method [21]. A novel approach for texture image retrieval is proposed by M. Kokare et al using a new set of two-dimensional (2-D) rotated wavelet filters (RWF) and discrete wavelet transform (DWT) jointly. A new set of 2-D rotated wavelet improves characterization of diagonally oriented textures. Experimental results indicate that the proposed method improves retrieval rate compared with the traditional DWT based approach. The proposed method also retains comparable levels of computational complexity [22]. Salient point detection in images is very useful for image processing applications like image compression, object detection and object recognition. It is also frequently used to represent local properties of the image in content-based image retrieval (CBIR). Many research results focused on finding the most salient points in the image. However, the large number of salient points and continuous point sets are still the problems. Based on the saliency values from wavelet-based methods, Yao Tsai presents a hierarchical algorithm for selecting the most salient points such that they cannot only give a satisfying representation of an image, but also make the image retrieval systems more efficiently. Under a top down approach from quadtree data structure, the algorithm keeps the most salient points in each quadrant according to the percentage of saliency values in the whole image. The performance of method was evaluated with the spreading measure and retrieval rate from a CBIR system. This method is robust and the extracted salient points provide efficient retrieval performance comparing with two wavelet-based point detectors [23]. J. Han et al present a novel five-stage image retrieval method based on salient edges. In this algorithm the canny operator is performed to detect edge points. Then, the Water- Filling algorithm is employed to extract edge curves. Then salient edges are selected and the shape features in terms of the salient edges are yielded. A similarity measure, integrated salient edge matching, that integrates properties of all the salient edges, is introduced, and used to compare the similarity of the query image with the images in the database. Finally, the best matches are returned in similarity order. The presented approach is easy to implement and can be efficiently applied to retrieve images with clear edges. [24, 25]. M. Herraez et al presents a novel approach to combining features when using multiimage queries consisting of positive and negative selections. A fuzzy set is defined so that the degree of membership of each image in the repository to this fuzzy set is related to the user s interest in that image. Positive and negative selections are then used to determine the degree of membership of each picture to this set. The system attempts to capture the meaning of a 9

7 selection by modifying a series of parameters at each iteration. The algorithm is easy to use and yields the highest performance in terms of the average number of iterations required to find a specific image. However, it is computationally more expensive and requires more memory than two of the other techniques [26]. G. Quellec et al propose content-based image retrieval (CBIR) method for diagnosis aid in medical fields. In the proposed system, images are indexed in a generic fashion, without extracting domain-specific features: a signature is built for each image from its wavelet transform. These image signatures characterize the distribution of wavelet coefficients in each subband of the decomposition. A distance measure is then defined to compare two image signatures and thus retrieve the most similar images in a database when a query image is submitted by a physician. To retrieve relevant images from a medical database, the signatures and the distance measure must be related to the medical interpretation of images, they propose to adapt the wavelet basis, within the lifting scheme framework, and to use a custom decomposition scheme. Weights are also introduced between subbands. All these parameters are tuned by an optimization procedure, using the medical grading of each image in the database to define a performance measure [27, 28]. It is not feasible for systems that analyze images in real-time where the images are stored or added on an ongoing basis. Nidhi Singh et al propose a framework which is able to select the most appropriate features to analyze newly received images thereby improving the retrieval accuracy and efficiency. The algorithm comprises of designing feature vectors after segmentation which will be used in similarity comparison between query image and database images. The framework is trained for different images in the database and its performance is found to be quite satisfactory when compared with the performance of conventional methods of content based image retrieval [29]. 10