Abstract
We describe an approach to finding regions of a texture of interest in arbitrary images. Our texture detectors are trained only on positive examples and are implemented as autoassociative neural networks trained by backward error propagation. If a detector for texture T can reproduce an n ×n window of an image with a small enough error then the window is classified as T. We have tested our detectors on a range of classification and segmentation problems using 12 textures selected from the Brodatz album. Some of the detectors are very accurate, a small number are poor. The segmentations are competitive with those using classifiers trained with both positive and negative examples. We conclude that the method could be used for finding some textured regions in arbitrary images.
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References
Datta, R., Joshi, D., Li, J., Wang, J.Z.: Image retrieval: Ideas, influences, and trends of the new age. ACM Computing Surveys 40(2), 1–60 (2008)
Wagner, P.: Texture analysis. In: Jahne, B., Haussecker, H., Geissler, P. (eds.) Handbook of Computer Vision and Applications, vol. 2, pp. 275–308. Academic Press, San Diego (1999)
Brodatz, P.: Textures: A Photographic Album for Artists and Designers. Dover, NY (1966)
Song, A., Ciesielski, V.: Fast texture segmentation using genetic programming. In: Sarker, R., et al. (eds.) Proceedings of the 2003 Congress on Evolutionary Computation CEC 2003, Canberra, December 8-12, pp. 2126–2133. IEEE Press, Los Alamitos (2003)
Song, A., Ciesielski, V.: Texture segmentation by genetic programming. Evolutionary Computation 16(4), 461–481 (Winter 2008)
Feng, D., Yang, Z., Qiao, X.: Texture Image Segmentation Based on Improved Wavelet Neural Network. In: Liu, D., Fei, S., Hou, Z., Zhang, H., Sun, C. (eds.) ISNN 2007. LNCS, vol. 4493, pp. 869–876. Springer, Heidelberg (2007)
Teke, A., Atalay, V.: Texture Classification and Retrieval Using the Random Neural Network Model. Computational Management Science 3(3), 193–205 (2006)
Avci, E.: An expert system based on Wavelet Neural Network-Adaptive Norm Entropy for scale invariant texture classification. Expert Systems with Applications 32(3), 919–926 (2007)
Scholkopf, J., Platt, J., Shawe-Taylor, J., Smola, A., Williamson, R.: Estimating the support of a hige-dimensional distribution. Neural Computation 13, 1443–1471 (2001)
Manevitz, L.M., Yousef, M.: One-class SVMs for document classification. J. Mach. Learn. Res. 2, 139–154 (2002)
Gondra, I., Heisterkamp, D., Peng, J.: Improving image retrieval performance by inter-query learning with one-class support vector machines. Neural Computing & Applications 13(2), 130–139 (2004)
Wu, S.X., Banzhaf, W.: Combatting financial fraud: A coevolutionary anomaly detection approach. In: GECCO 2008: Proceedings of the 10th annual conference on Genetic and evolutionary computation, pp. 1673–1680. ACM, New York (2008)
Curry, R., Heywood, M.: One-class learning with multi-objective genetic programming. In: IEEE International Conference on Systems, Man and Cybernetics, ISIC, pp. 1938–1945 (2007)
Brodatz Texture Database, http://www.ux.his.no/~tranden/brodatz.html (Visited 3-June-2009)
Zell, A.: Stuttgart neural network simulator, http://www.ra.cs.uni-tuebingen.de/SNNS/ (Visted 3-June-2009)
Corel Corporation: Corel professional photos CD-ROMs, vol. 12 (1994)
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Ciesielski, V., Ha, V.P. (2009). Texture Detection Using Neural Networks Trained on Examples of One Class. In: Nicholson, A., Li, X. (eds) AI 2009: Advances in Artificial Intelligence. AI 2009. Lecture Notes in Computer Science(), vol 5866. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10439-8_15
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DOI: https://doi.org/10.1007/978-3-642-10439-8_15
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