Texture Features for Image Retrieval using Wavelet Transform

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1 Proceedings of the International Conference on Cognition and Recognition Texture Features for Retrieval using Wavelet Transform Vaishali Pawar and Milind Mushrif Department of Electronics and Communication, Yashwantrao Chavan College of Engg Nagpur. Abstract Accuracy and efficiency are two important issues in designing content-based image retrieval system. In this paper we present an approach on a wavelet transform called tree-structured transform or wavelet packets for texture analysis. A simple texture classification algorithm having excellent performance for dominant channels decomposition tree structured is proposed here. The performance of our method is compared with the full-decomposition tree-structured and dimensionally reduced tree structure. 1. INTRODUCTION Due to rapid increase in tremendous amount of digital image collections, various techniques for storing, browsing, retrieving images have been investigated in recent years. The traditional approach to image retrieval is to annotate images by text and then use text based database management systems to perform image retrieval. There are several drawbacks of using keywords to achieve visual information. The keywords become inadequate for large database and it is difficult to phrase each of the images. To overcome difficulties encountered by a text based image retrieval system, Content Based Retrieval (CBIR) was proposed in early 1990 s [1]. In CBIR, the system can discriminate and retrieve images on their visual contents such as colors, shapes, textures, or the rotation among the objects. A color and spatial features with multiscale color histograms [2] [3]; a quadratic color descriptor referring to dominant color features [4], the frequency layered color indexing techniques [5], Fuzzy color histogram [6] can be used for image discrimination. The CBIR interest has been growing in surface inspection [7], medical images [8] and real-time videos retrieval [9]. Examples of the systems are cries [10], pichunter [11]. To reduce the semantic gap; iterative refinement by relevance feedback CBIR systems [12] improves the quality of the result. The compressed image retrieval [13], the watermarking in image retrieval [14], Nearest-Neighbor methods [15], occupancy model for histogram based similarity evaluation [16], and the adaptive learning [17] are the new approaches to CBIR. Texture is an important property of many types of images. There is no universally agreed definition of what texture is [17]. Features of texture include roughness, granulation and regularity. There is a large need to classify images based on textural features in various fields like scene analysis, medical image analysis etc. Wavelet transform achieve s consistently good performance and ranks among the best approaches. Simple features like mean, variance and moments are extracted from wavelet sub bands at each wavelet decomposition level. The gradient vectors and energy density strings [18] at sub bands can also achieve the efficient results. In this paper, two dimensional wavelet transform is applied to texture analysis. The wavelet packets and full decomposition tree-structured are analyzed with different distance measures. A simplified algorithm for dominant channels is presented here. The paper is organized as follows. In section 2 architecture of CBIR system is discussed. Section 3 gives an idea of tree structured wavelet. Section 4 elaborates feature dimensionality reduction methods. Section 5 is centered on dominant channel wavelets, and in section 6 image retrieval scheme is discussed. Section 7 explains the distance measures. 2. THE ARCHITECTURE OF A CBIR SYSTEM Content Based Retrieval [CBIR] system consists of two parts Collection and Retrieval as shown in figure 1 [1]. The design of architecture requires careful analysis of three fundamental components: a feature transformation, feature representation and similarity function. 219

2 Texture Features for Retrieval using Wavelet Transform New Compute feature Feature file database Query Compute feature Compute similarity between features Display image Fig. 1: Basic Algorithmic Components of Query by Pictorial Example collection : The features computed are stored in a feature file and new image is registered in im age database. retrieval: To compute feature of a query image and find similar images from the database. Similarity evaluation block compares visual data objects for approximate or similar matching (rather than exact matching). It becomes a primary procedure in visual information retrieval. The architecture of texture retrieval system using wavelet decomposition in shown in fig 2. (a) and (b). The feature extraction is always required for both image collection and image retrieval. The feature block is replaced by Wavelet decomposition block. In our system, the feature file consists of energy parameters, i.e mean and standard deviation. Also channel string is used as a parameter for dominant channels retrieval system. The query feature file is used to find the matching feature file from the database, computed by the distance-measured techniques. The best-matched images are displayed. New Wavelet decompositi on registration Feature File database Query Wavelet decomposition Sorting Feature file Similarity algorithm database Display Matched images Fig. 2: The Architecture of Texture Retrieval System, (a) Collection, (b) Retrieval 3. TREE STRUCTURED WAVELET TRANSFORM The traditional pyramid -type wavelet transform recursively decomposes sub signals in the low frequency channel. Since the most significant information of a texture often appears in the middle frequency channel, further decomposition just in the lower frequency region may not help much for the purpose of classification [19]. The appropriate method would be to apply decomposition to all the sub bands LL, LH, HL and HH [19]. The three level decomposition of tree structured wavelet transform is shown in figure 3. This is a fixed structure where decomposition tree can be obtained by sequentially decomposing the LL, LH, HL, and HH sub bands. In fig.3, nodes represent a (approx.), h (horizontal), v (vertical) d (diagonal) and the R2 and R3 stands for second and third level of wavelet decomposition respectively. An image X is decomposed into a, h, v, d sub bands at first level. Further these sub bands are used as an input for 2-D wavelets thus resulting in 4 sub bands each. This is a second level of decomposition. At the third level of tree, the result of second level is fed as an input and so on. 4. FEATURE DIMENSIONALITY REDUCTION METHOD Number of methods has been proposed for Dimensionality reduction. [20] [19] [17]. The reduction of feature vector dimensions should preserve the characteristics of coefficients obtained from wavelet decomposition. The wavelet transform with three levels as shown in fig 3, results in 4x1+4x4+4x16= 84 sub bands. The length of feature vector is equal to 84 x number of feature parameters used in combination. 220

3 Proceedings of the International Conference on Cognition and Recognition Fig. 3: Tree Structured wavelet transform Decomposition Scheme Other method suggested by Manjunath and Ma [21] for similar tree structured decomposition is im plemented by decomposing all sub bands except HH. This decomposition results in 4x1+4x3+4x9=52 sub bands. The feature vector length for this results in 52 x nos. of features. The decomposition of all sub bands for three-level tree results in 84 sub bands. The dimensionality reduction proposed method [19] results in 28 sub bands for a same three-level tree. Detailed procedure is as follows Step 1 Concatenate all approximate coefficients and similarly go for LH, HL, HH. E.g. (a1; aa2; aaa3), (ad2; ada3) and so on. Step 2 Concatenate all the horizontal detail coefficients e.g. (aah3; ahh3; avh3; adh3) Step 3 Concatenate all the vertical detail coefficients e.g. (aav3; ahh3; avv3; adv3) Step 4 Concatenate all the diagonal detail coefficients e.g. (aad3; ahd3; a vd3; add3) Step 1 results in 16 vectors and steps 2, 3, 4 result in 4 vectors each. So, the length of the feature vector is = 28 features. The dimension of feature space will not increase even when the number of decomposition levels increases. The feature space can further be reduced if HH sub bands are not decomposed. It results in 21 sub bands. 5. DOMINANT CHANNEL WAVELET TRANSFORM To avoid full decomposition, one of the criteria suggested by T. Chang [20] is used. It is usually unnecessary and expensive to decompose all sub signals in each scale to achieve full decomposition. The energy of image sub bands at each level is used to decide further decomposition of that band. If the energy of sub bands is significantly smaller than others, that region is not decomposed further since it contains less information. Another stopping criterion is that the size of smallest sub image should not be less than 16x16. This tree-structured wavelet provides a non-redundant representation and takes very less space to store the features. The meaning of the channel strings is shown in fig 4 (a) and (b). The algorithm for decomposition is given below. 5.1 Algorithm 1: Tree-structured Wavelet Transform Decompose a given textured image with 2-D (Two dimensional) wavelet transform into 4 Sub-images, which can be used as parent and children nodes in a tree. Calculate the energy of each decomposed image (child node). If x is a decomposed image with size (m, n) then the energy is: 221

4 Texture Features for Retrieval using Wavelet Transform e = 1 mn m n i = 1 j = 1 x ( i, j ) If the energy of sub-image is significantly smaller than others, stop the decomposition in that region. This step can be achieved comparing the energy with the largest energy value in the same scale. This is, if e < c*emax, stop decomposing that region where c is a constant less than 1. If the energy of a sub-image is significantly large, then apply the above decomposition procedure to the sub -image. I Channel ah a h v d H(H,L) a h v d a h v d V(L,H) D(H,H) ahhh Fig. 4: (a) The meaning of channel ah Fig. 4: (b) Tree structured transform domain 6. IMAGE RETRIEVAL PROCEDURE The texture database used in experimentation consists of 78 different textures from brodartz album. Size of each texture is 640x640 pixels. Each of 640x640 images are divided into twenty five 128x128 non overlapping sub images, thus creating a database of 1950 patterns in a database. Daubhechies wavelet filter (db4) coefficients are used for computing tree-structured wavelet transform. The combination of standard deviation and energy is used as a feature parameters corresponding to each of the sub bands are used. First set of features has 84 sub bands and the feature vector length is 84x number of feature parameters, i.e. 84x2. Second set of features for dimensionality reduction and the feature vector length is 28 x number of feature parameters, i.e. 28x2. Third set of features are with dimensionality reduction but without HH sub bands. It leads to feature vector length as 21x2 (number of features). Fourth set of features are for dominant channel vectors. The classification algorithm for dominant channels image collection system and image retrieval system is discussed next. 6.1 Algorithm 2: Classification Algorithms Collection System for Dominant Channels As per the algorithm 1, determine the tree structure of an image and save the dominant channels. For M samples obtained from the same texture, decompose each sample with tree-structured wavelet transform and calculate the normalized energy map. A representative energy map for each texture is generated by averaging the energy map over all M samples. Repeat the process for all textures. Retrieval for Dominant Channels Decompose the query texture with the tree-structured wavelet transform and construct its energy map. The maximum number of dominant channel string stored is eight along with their energy maps. For retrieval procedure pick first four dominant channels and match with the database feature file. 222

5 Proceedings of the International Conference on Cognition and Recognition If there is a match for first four dominant channels, then check where the distance between their energy maps are almost zero. If the distance between energy maps doesn t lead to zero, then repeat step 2 for other feature files. But if it is not so, then find the normalized energy stored in the feature file. Retrieve the images whose distance between extracted normalized energy is less. The retrieval process depicted here is very simple than that proposed in [19]. The classification algorithm proposed in [19] uses channels with large energy values as features. Arranging them in an order, the k th dominant channel is used for detection of nearest distance. If this distance is greater than the minimum value of the ordered candidate list, then that candidate texture was removed from the list. The procedure is repeated until a single texture is matched. This algorithm is very complex as the number of iterations required is large. On the other hand the algorithm presented here uses only first four dominant channel strings for distance calculation. A single match is obtained by checking the first four strings. This match is further used for retrieving the images whose normalized energy s are equal. 7. DISTANCE MEASURES Given two feature vectors fx and fy from the same transform, where fx is a query feature vector and fy is the feature vector from database, then the Manhattan distance between them is given as: D ( x. y ) = n i = 1 fx i fy i where n is the length of feature vector n will vary for 84, 28 and 21 sub band sets. The another distance measure used for retrieval is the Euclidean distance method, D ( x. y ) = n i = 1 ( 2 fx i fy 2 i ) 8. EXPERIMENTAL RESULTS The 78 textures forming a total of 1950 images is in a database, were compared for retrieval accuracy, using energy and standard deviation as a feature parameter. Table 1 presents the retrieval accuracy for a query image. The result is against the total 28 images displayed out of which matched for 21 sub bands, 28 sub bands, 84 sub bands and Domi are listed respectively in S-21, S-28, S-84 and S-Domi. Table 1 gives results of Euclidean distance measure and also result of Manhattan distance measure for the 20 selected images. Table 2 presents the retrieval time for the two distance measures. Query Table 1: Retrieval accuracy for 78 textures S -21 S -28 S -84 S -domi Euclidean Manhattan Euclidean Manhattan Euclidean Manhattan Manhattan D1 48% 92% 48% 92% 36% 72% 100% D101 96% 88% 96% 88% 96% 92% 100% D102 76% 92% 76% 92% 72% 92% 100% D16 100% 100% 100% 100% 96% 100% 100% D18 52% 68% 52% 72% 48% 72% 100% D21 100% 100% 100% 100% 100% 100% 100% D25 76% 92% 76% 96% 68% 92% 100% D26 88% 100% 88% 100% 76% 100% 100% D32 80% 96% 80% 96% 72% 96% 100% D47 68% 80% 68% 88% 60% 76% 100% 223

6 Texture Features for Retrieval using Wavelet Transform Query S -21 S -28 S -84 S -domi Euclidean Manhattan Euclidean Manhattan Euclidean Manhattan Manhattan D49 100% 100% 100% 100% 100% 100% 100% D51 56% 88% 56% 88% 40% 68% 100% D53 96% 100% 96% 100% 96% 100% 100% D64 36% 76% 72% 100% 68% 100% 100% D65 68% 100% 72% 100% 60% 96% 100% D68 80% 96% 80% 96% 68% 96% 100% D71 48% 76% 36% 76% 36% 72% 100% D83 96% 96% 96% 96% 80% 96% 100% D87 88% 92% 88% 96% 84% 92% 100% D % 100% 100% 100% 100% 100% 100% Query Euclidean Table 2: Retrieval time for 78 textures R-21 R-28 R-84 R-domi Manhattan Euclidean Manhattan Euclidean Manhattan Euclidean D D D D D D D D D D D D D D D D D D D

7 Proceedings of the International Conference on Cognition and Recognition As the dimensionality reduction has increased complexity the retrieval time for 28 and 21 sub bands is more than that of 84 sub bands. Dominant channel has the similar retrieval time that of 84 sub bands, but its accuracy has been improved. The retrieval accuracy of 28 sub bands with (HH) is improved over 84 sub bands. 9. CONCLUSION It is shown that the tree-structured wavelet provides a good analytic tool for texture analysis. The dimensionality reduction of feature vector of tree-structured wavelet transform decomposition scheme is also proposed in this paper. Large database of 1950 images is used for checking the retrieval performance of the features. The standard deviation and energy features are used in combinations of Euclidean and Manhattan distance functions. The results are quite impressive, and also the feature vector length is reduced, without increasing the retrieval time for the dominant channels. In future, it would be interesting to see a comparison of these methods with the other methods based on dominant channels and dimensionality reductions. REFERENCE [1] Arnold W.M. Smeulders, Senior Member, IEEE, Marcel Worring, Simone Santini, Member, IEEE, Amarnath Gupta, member IEEE and Ramesh Jain, Fellow, IEEE Content Based Retrieval at the end of the early years, IEEE Transactions On Pattern Analysis And Machine Intellegence, Vol 22, December [2] Ing-Sheen Hsieh and Kuo -Chin Fan, Multiple Classifiers for Color Flag and Trademark Retrieval, IEEE Transactions on Processing, Vol. 10, June [3] Anning Ouyang and Yep -Peng Tan, A Novel Multi Scale Spatial-Colr Descriptor for Content Based Retrieval 7th international conference on control, automation. Robotics and vision [ICARCV 02], Singapore, DEC [4] Yining Deng, Member, IEEE, B.S. Manjunath, Member IEEE, Charles Kenney, Michael S. Moore, Student Member, IEEE and Hyundoo Shi An Efficient Color Representation for Retrieval, IEEE Transactions On Processing, Vol. 10, January [5] Guoping Qiu and Kin-Man Lam, Frequency Layered Color Indexing for Content Based Retrieval IEEE Transactions On Processing, Vol. 12, January [6] Ju-Han ad Kai-KuangMa, Furry Color Histogram and its use in Content Retrieval, IEEE Transaction On Processing, Vol.11 [2002] [7] Jukka Iivarinen, Jussi Pakkanen and Juhani Rauhamaa Content Based Retrieval in Surface Inspection, 7th international conference on control, automation. Robotics and vision [ICARCV 02] DEC 2002, Singapore. [8] A Content Based Retrieval System for Medical s, seventh internat ional conference on control, automation. Robotics and vision [ICARCV 02] DEC 2002, Singapore. Tratjana Zrimec. [9] J.R. Wang, R. Wang, N. Parameswaran, J.S. Jin Browsing Videos Online Using Semantic Information, 7th international conference on control, automation. Robotics and vision ICARCV 02] DEC 2002, Singapore. [10] Qasim Iqbal and J.K. Aggrawal CIRES, A System for Content -Based Retrieval In Digital Libraries, 7th international conference on control, automation, robotics and vision [ICARCV 02,] DEC 2002, Singapore. [11] Ingemar J. Cox, Senior Member, IEEE, Matt L. Miller, Thomas P. Minka, Thomas V. Papathamos and Peter N. Yianilos, Senior Member, IEEE The Bayesian Retrieval System, PicHunter: Theory, Implementation and Psychophysical Experiments, IEEE Transactions On Processing, Vol. 22, December [12] Horst Eidenberger and Christian Breiteneder Semantic Feature Layers in Content Based Retrieval Implementation of Human World Features. ICARCV 02 DEC 2002, Singapore. [13] Guocan Fenga and Jianmin Jianga, JPEG Compressed Retrieval via Statistical Features Pattern Recognition 36 [2003] [14] Xuelong Li, Watermarking in Secure Retrieval, Pattern Recognition Letters 24 (2003) [15] David W. Jacobs, Member, IEEE Computer Society, Classification with Nonmetric Distances Retrieval and Class Representation, IEEE Transactions on Pattern Analysis and Machine Intellegence, Vol. 22, June [16] Donald A. Adjeroh and M.C. Lee, An occupancy Model for Retrieval and Similarity Evaluation IEEE Transactions on Processing, Vol. 9, January

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