Abstract

Texture is an important perceptual property of images based on which image content can be characterized and searched for in a Content Based Search and Retrieval (CBSR) system. This paper investigates techniques for improving texture recognition accuracy by using a set of Wavelet Decomposition Matrices (WDM) in conjunction with Grey Level Co-occurrence Matrices (GLCM). The texture image is decomposed at 3 levels using a 2D Haar Wavelet and a coefficient computed from the decomposition matrices is combined with features derived from a set of normalized symmetrical GLCMs computed along four directions, to provide improved accuracy. The proposed scheme is tested on a set of 13 textures derived from the Brodatz database and is seen to provide accuracies of the order of 90%.

References

• Laws, K. I. 1980. Textured image segmentation. Doctoral Thesis. University of Southern California, Los Angeles, CA, USCIPI Rep. 940.
• Conners, R. W., Trivedi, M. M., and Harlow, C. A. 1984. Segmentation of a High Resolution Urban Scene using Texture Operators. Computer Vision, Graphics and Image Processing, vol. 25, 273-310.
• Dinstein, I., Fong, A. C., Ni, L. M., and Wong, K. Y. 1984. Fast Discrimination between Homogeneous and Textured Regions. In Proceedings of the 7th International Conference on Pattern Recognition, Montreal, Canada, 361-363.
• Wang, R., Hanson, A. R. and Riseman, E. M. 1986. Texture Analysis based on Local Standard Deviation of Intensity. In Proceedings of the Conference on Computer Vision and Pattern Recognition, Florida, USA, 481-488.
• Tamura, H., Mori, S., and Yamawaki, T. 1978. Textural Features corresponding to Visual Perceptions. IEEE Transactions on Systems, Man and Cybernetics, vol. 8, no. 6, 460-473.
• Pentland, A. P. 1984. Fractal based Description of Natural Scenes. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 6, no. 6, 661-674.
• Khotanzad, A., and Bouarfa, A. 1988. A Parallel Non-parametric Non-iterative Clustering Algorithm with Application to Image Segmentation. In Proceedings of the 22nd Asilomar Conference on Signals, Systems and Computers, Pacific Grove, CA, 305-309.
• Huang, N. K. 1984. Markov Model for Image Segmentation. In Proceedings of the 22nd Allerton Conference on Communication, Control and Computing, Montecello, 775-781.
• Bovik, A. C., Clark, M., and Geisler, W. S. 1990. Multichannel Texture Analysis using Localized Spatial Filters. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 12, no. 1, 55-73.
• Manjunath, B. S., and Chellappa, R. 1993. A Unified Approach to Boundary Perception : Edges, Textures and Illusory Contours. IEEE Transactions on Neural Networks, vol. 4, no. 1, 96-108.
• Deguchi, K., and Morishita, I. 1978. Texture Characterization and Texture-based Image Partitioning using Two-dimensional Linear Estimation Techniques. IEEE Transactions on Computers, vol. C-27, no. 8, 739-745.
• Haralick, R. M. 1979. Statistical and Structural Approaches to Texture. Proceedings of IEEE, vol. 67, 786–804.
• Graps, A. 1995. An Introduction to Wavelets. IEEE Computational Science and Engineering, vol. 2, no. 2, 50-61.
• Brodatz, P. 1966. Textures : A photographic album for artists and designers. Dover Publications, NY. (http://www.ux.uis.no/~tranden/brodatz.html).