Performance study of Gabor filters and Rotation Invariant Gabor filters

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1 Performance study of Gabor filters and Rotation Invariant Gabor filters B. Ng, Guojun Lu, Dengsheng Zhang School of Computing and Information Technology University Churchill, Victoria, 3842, Australia Abstract Gabor filters have been proven to be very useful for texture retrieval and are widely adopted However, the original Gabor texture features are rotation variant. Recently, Zhang et proposed rotation normalization using circular shift. They have shown the proposed rotation normalization is effective through some examples, but did not comprehensively study its performance. The purpose of this paper is to study the performance of the rotation normalization on a good size texture database. Our experimental results show that the proposed rotation normalization is effective in retrieving rotated textures and has some adverse effect on retrieving nonrotated texture. Keywords: Gabor filter, 1. Introduction CBIR, texture and rotation Gabor filters have been shown to be able to capture image features, which reproduce responses that are quite similar to that of the human visual cortical cells Their psychophysical resemblance to the biological visual system has enabled Gabor filters to yield good results content-based image retrieval (CBIR) applications. Manjunath and Ma have shown that image retrieval based on Gabor features outperforms others using Pyramid-structured wavelet transform (PWT) features, Tree-structured wavelet transform (TWT) features and multi-resolution simultaneous autoregressive model (MR- SAR) features. Recently, Zhang et [7] proposed a rotation normalization method for texture retrieval. It has been found that the rotation normalization is effective for retrieving rotated texture images. But, Zhang et [7] proposed approach has only tested some individual textures. There is no performance comparison between the original Gabor feature and rotation normalized features Therefore, it s not clear if the rotation normalization has adverse effect on retrieving large database of non-rotated, rotated and mixed texture textures. It is purpose of this paper to study these. The rest of the paper is organized as follows: Section 2 briefly describes the 2D Gabor filters (wavelets), which include distance calculation. Section 3 discusses rotation normalization by Circular-Shift. Section 4 describes our proposed performance study. In Section 5, we present the experimental results and analysis. Section 6 concludes the paper. 2. Texture Retrieval based on Gabor Features For a given image with size its discrete Gabor wavelet transform is given by a convolution where, and t are the filter mask size variables, and is the complex conjugate of which is a class of selfsimilar functions generated from dilation and rotation of the mother Gabor wavelet. m and n specify the scale and orientation of the wavelet respectively, with m = 0, 1,.. n = 0, 1,.. and number of scales is M, N for number of orientations. After applying Gabor filters on the image with different orientation at different scales, we obtain an array of the filtered images. 0, 1, n =0, These magnitudes represent the energy content at different scale and orientation of the original images. The main purpose of texture-based retrieval is to find images or regions with similar texture. It is assumed that we are interested in images or regions that have homogenous texture. The following mean and standard deviation of the magnitude of the transformed coefficients are used to represent the homogenous texture feature of the region:

2 A vector (texture representation) is created using and as the feature components scales and N orientations are used and the feature vector isgiven by:. this paper, the number of scale (M) and orientation is set be 5 and 6 respectively, as commonly in the previous The filter mask size of is be We the City Block distance measurement, also as the Manhattan distance measurement, to measure distance between texture features: where, Q = {Qo,. and = {To,TI, are the query and target feature vectors respectively. Since the above texture feature not rotation invariant, similar textures with different orientation may have very different feature vectors, leading to very large distance. example, images in Figure and are the same with different orientations. They will have very distance if the measurement is applied directly. Zhang et al proposed a circular shift on the vectors to the rotation variant problem. Specifically, the total energy for each orientation was calculated. The orientation with the highest total energy is called the dominant feature elements are then moved the dominant direction to become the first elements The other elements are circularly shifted accordingly. For instance, if the original feature vector is and is at the dominant direction, then the feature vector will be This method assumed that compare similarity between two they should rotated their dominant directions are the same. To study the of the proposed rotation method, we compare the texture retrieval using the original feature vectors and normalized feature on texture databases with rotated and textures. want find out hat is the effect of the rotation on rotated textures? What is the effect of the rotation textures? The explanation above effects. The 1,792 Brodatz textures are created from 112 categories of 512x512 with each category subdivided into 16 smaller textures 128 x 128. We carried out experiments. For experiment have been created each category of the original texture database in the following combination of (a) - 4 textures rotated to 30 degrees - 4 textures rotated to degrees - 4 textures rotated 90 degrees - 4 rotated to 120 degrees The database consists of 25% of each experiment the original database of,792 rotated textures are used. In each experiment, each texture (a total of 1,792) is used as a query each of the 2 combinations with and without rotation

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4 rotated 90 degrees is very small, so the Gabor features without rotation normalization can retrieve this types of features. --, -- RECALL - - ROTATED-w ROTATED-w Figure 6. Recall and Precision Chart - Each category of 16 similar textures has a combination of Rotated textures - 30 degrees x 4, 60 degrees x 4; 90 degrees x 4; 120 degrees x Experimental results on non-rotated textures experiments we compare texture retrieval performance of Gabor features with and without the rotation normalization on the database of non-rotated textures. Figure 7 shows an example of texture retrieval with Gabor features without rotation normalization. Given the query texture is non-rotated, all 15 similar non-rotated textures has been retrieved with good ranking from 1 to 15. Fiaure 7. Screen 1, Textures retrieval using Gabor without Circular-Shift feature on database with all non-rotated images; Top-left corner is the query texture for category Figure 8 shows an example of texture retrieval with Gabor features with the rotation normalization.. Given the * - texture is non-rotated, though all 15 similar rotated textures has been their ranking is relatively low from ranking 1 to 10, followed by 13, 15, 19 shown in screen 1, with screen 2 consisting of ranking 36 and 37. Figure 8. Screen 1, Textures retrieval using Gabor filters with Circular-Shift feature on brodatz database with all non-rotated images; Top-left corner is the query texture for category

5 These retrieval results in Figure 7 and 8 show the rotation normalization has some adverse effect of retrieval performance for the database of non-rotated textures. Figure 9 shows the recall-precision curves averaged over the 1792 queries for the two cases. It can been seen that the Gabor feature with rotation normalization performs slightly worse on the non-rotated textures than the original features, The above results can be explained as follows. Some textures don t have a single dominant direction. For example, the query in Figures 7 and 8 has two dominant directions of similar strength. So when we apply the rotation normalization, the sixteen textures of the same category may be rotated into different directions due to slight differences in the texture pattern, leading to large distances among some of these sixteen textures. - - N0N-ROTAT RECALL NON-ROTATED-With-CI Figure 9. Recall and Precision Chart All images in each category of 16 images are at 0 degree rotated). 6. Discussion and Conclusions Through our experiments and analysis, we show that the rotation normalization using circular shift is effective in retrieving similar but rotated textures. However, it has slight adverse effect on non-rotated textures, especially on those textures that don t have a single dominant direction. Based on the above finding, we recommend that the rotation normalization should only be used when we are quite sure that the database contains some rotated textures. Alternatively, the rotation normalization should be provided as an option for user to try and choose. FT (Special Issue on Digital Libraries), Vol. August 1996, [2] John R. Smith. Integrated Spatial and Feature Image System: Retrieval, Analysis and Compression. thesis, Columbia University, [3] Yining Deng. A Region representation for Image and Video Retrieval. thesis, University of California, Santa Barbara, [4] Wei-Ying Ma. Netra: A Toolbox for Navigating Large Image Databases. thesis, University of California, Santa Barbara, [5] Sylvie Jeannin Visual Part of Experimentation Model Version 5.0. Nordwijkerhout, March [6] Alexander Rotation Invariant Texture Description using General Moment Invariants and Gabor Filters. In Proc. Of the Scandinavian Conf. On Image Analysis. Vol I. June, 1999, pp. [7] Dengsheng Zhang, Aylwin Wong, Maria Indrawan, Guojun Lu Content-based Image Retrieval Using Gabor Texture Features, Gippsland School of Computing Information Technology, Monash University. S. description of the responses of simple cortical cells. Journal of the Optical Society of America, [9]Nick Efford, Digital image processing: a practical introduction using Java, Harlow New York : Addison-Wesley, Lianping Chen, Guojun Lu, Dengsheng Zhang, Effects of Different Gabor Filter Parameters on Image Retrieval by Texture, Gippsland School of Computing and Information Technology Monash University Churchill, Victoria, 3842, Australia. [ Rubner and Tomasi, Perceptual metrics for image database navigation, Boston, Mass.; London: Kluwer Academic, [ Dengsheng Zhang, Guojun Content-based image retrieval using Gabor texture features, In Proc. Of First IEEE Pacific- rim Conference on Multimedia (PCM OO), ND, USA, June 1-3,2001, [ Dengsheng Zhang, Guojun Lu, Evaluation of Similarity Measurement for Image Retrieval, Gippsland School of Computing and Info Tech, Monash University, Churchill, Victoria Dengsheng Zhang, Image Retrieval based on Shape, thesis, Gippsland School of Computing and Info Tech, Monash University, Churchill, Victoria 3842, March References B. Manjunath and W. Y. Ma. Texture features for browsing and retrieval of large image data IEEE Transactions on Pattern Analysis and Machine Intelligence,