Clustering Stability-Based Feature Selection for Unsupervised Texture Classification

• Autorzy: Klepaczko A. , Materka A.
• Języki publikacji: EN

Abstrakty

EN

This paper addresses the issue of unsupervised texture image classification. This kind of analysis can augment automatic image interpretation and recognition whenever visualized objects surfaces reveal some regular shapes or patterns. The variety of texture models and parameters calculated therein implies the need to select the relevant attributes which allow the best possible texture discrimination. However, it has been observed that the discriminative power of the texture parameters deteriorates if the image dimensions are small relative to the size of a single texture element. In that case, feature vectors corresponding to different textures become less distinguishable. Although this does not constitute a significant impediment to supervised feature selection, the methods which operate in an unsupervised manner and are analyzed in this study perform well only if the images being classified contain a large portion of texture. We illustrate this phenomenon through a series of experiments with natural, Brodatz-album texture images. We also show how to overcome the outlined problem by assessing features saliency using a measure based on the notion of clustering stability.

Słowa kluczowe

EN

texture analysis; unsupervised feature selection; clustering stability

• Wydawca: Institute of Information Technology of the Warsaw University of Life Sciences – SGGW
• Czasopismo: Machine Graphics and Vision
• Rocznik: 2009
• Tom: Vol. 18, No. 2
• Strony: 125--141
• Opis fizyczny: Bibliogr. 31 poz., il., wykr.

Twórcy

autor

Klepaczko A.

autor

Materka A.

• Technical University of Lodz, Institute of Electronics, Medical Electronics Division 90-924 Lodz, ul. Wolczanska 211/215, Poland

Bibliografia

• [1] Brodatz P.: Textures: A Photographic Album for Artists and Designers. New York, Dover Publications. 1966.
• [2] Davies D. L., Bouldin W.: A cluster separation measure. IEEE Trans. Pattern Analysis and Machine Intelligence 1(4), 224-227, 1979.
• [3] Goldberg D. E.: Genetic Algorithms in Search Optimization and Machine Learning. Addison-Wesley, Reading, MA. 1989.
• [4] Du L. J.: Texture Segmentation of SAR Images Using Localized Spatial Filtering. In: Proceedings of International Geoscience and Remote Sensing Symposium, pp. 2005-2008, Washington, DC. 1990.
• [5] Fukunaga K.: Statistical Pattern Recognition (2nd edition). Academic Press, San Diego, CA. 1990.
• [6] Tuceryan M., Jain A. K.: Texture Segmentation Using Voronoi Polygons. IEEE Trans. PAMI, 12(2), 211-216, 1990.
• [7] Jain A. K., Farrokhnia F.: Unsupervised Texture Segmentation Using Gabor Filters. Pattern Recognition, 24, 1167-1186, 1991.
• [8] Swets D., Weng J.: Using Discriminant Eigenfeatures for Image Retrieval. IEEE Trans. PAMI, 18(8), 831-836, 1996.
• [9] Blum A. L., Langley P.: Selection of Relevant Features and Examples in Machine Learning. Artificial Intelligence 97(1-2), 245-271, 1997.
• [10] Kohavi R., John G. H.: Wrappers for feature subset selection. Artifical Intelligence, 97(1-2), 273-324, 1997.
• [11] Struyf A., Hubert M., Rousseeuw P. J.: Integrating robust clustering techniques in S-PLUS. Computational Statistics & Data Analysis 26, 17-37, 1997.
• [12] Smith G., Lange G. D.: Biological Cellular Morphometry - Fractal Dimensions, Lacunarity and Multifractals. In: Losa G. A., Merlini D., Nonnenmacher T. F., Weibel E. R. (eds.): Fractals in Biology and Medicine, Vol. 2, pp. 30-49, Birkhauser, Basel. 1998.
• [13] Tuceryan M., Jain A. K.: Texture Analysis. In: Chen C. H., Pau L. F., Wang P. S. P. (eds.): The Handbook of Pattern Recogntion and Computer Vision (2nd Edition), pp. 207-248, World Scientific Publishing Co. 1998.
• [14] Duda R. O., Hart P. E., Stork D. G.: Pattern Classification. John Wiley & Sons, Inc. 2001.
• [15] Jain A. K., Ross A., Prabhakar S.: Fingerprint matching using minutiae and texture features. In: Proceedings of the IEEE Int. Conf. Image Processing, vol. 3, pp. 282-285, 2001.
• [16] Ben-Hur A., Elisseeff A., Guyon I.: A stability based method for discovering structure in clustered data. In: Pacific Symposium on Biocomputing, pp. 6-17, Singapore, World Scientific. 2002.
• [17] Kim Y. S., Street W. N., Menczer F.: Evolutionary model selection in unsupervised learning. Intelligent Data Analysis 6, 531-556, 2002.
• [18] Morita M., Sabourin R., Bortolozzi F., Suen C. Y.: Unsupervised Feature Selection Using Multi-Objective Genetic Algorithms for Handwritten Word Recognition. In: Proceedings of the Seventh IEEE Conf. Document Analysis and Recognition, pp. 666-670, 2003.
• [19] Dy J. G., Brodley C. E.: Feature selection for unsupervised learning. J. Machine Learn. Res. 5, 845-889, 2004.
• [20] Klepaczko A., Materka A.: Clustering Quality Based Feature Selection Method. Int. J. Machine Graphics & Vision, 13 (4), 357-372, 2004.
• [21] Lange T., Braun M. L., Roth V., Buhmann J.: Stability-Based Validation of Clustering Solutions. Neural Computation, 16, 1299-1323, 2004.
• [22] Liu Y., Teverovskiy L., Carmichael O., Kikinis R., Shenton M., Carter C. S., Stenger V. A., Meltzer C. C.: Discriminative MR Image Feature Analysis for Automatic Schizophrenia and Alzheimer's Disease Classification. In: Proceedings of the 7th International Conference on Medical Image Computing and Computer Aided Intervention, pp. 393-401, 2004.
• [23] Oh I. S., Lee J. S., Moon B. R.: Hybrid Genetic Algorithms for Feature Selection: IEEE Trans. Pattern Analysis and Machine Intelligence 26 (11), 1424-1437, 2004.
• [24] Luxburg U. von, Ben-David S.: Towards a statistical theory for clustering, PASCAL Workshop on Statistics and Optimization of Clustering. 2005.
• [25] Bojar K., Nieniewski M.: Modelling the Spectrum of the Fourier Transform of the Texture in the Solar EIT Images. Int. J. Machine Graphics & Vision, 15(3/4), 285-296, 2006.
• [26] Handl J., Knowles J.: Feature Subset Selection in Unsupervised Learning via Multiobjective Optimization. Int. J. Computational Intelligence Research, 2(3), 217-238, 2006.
• [27] Kulikowski J. L., Przytulska M., Wierzbicka D.: Recognition of Textures Based on Multi-Level Morphological Spectra. In: IFMBE Proceedings, vol. 14, World Congress on Medical Physics and Biomedical Engineering, Seoul 2006, Springer, pp. 2164-2167, 2006.
• [28] Lerski R.: Clinical applications of texture analysis. In: Hajek M., Dezortova M., Materka A., Lerski R. (eds.): Texture Analysis for Magnetic Resonance Imaging, pp. 151-191, Med4publishing, Prague. 2006.
• [29] Materka A.: What is the texture? In: Hajek M., Dezortova M., Materka A., Lerski R. (eds.): Texture Analysis for Magnetic Resonance Imaging, pp. 11-43, Med4publishing, Prague. 2006.
• [30] Strzelecki M., Materka A., Szczypinski P.: MaZda. In: Hajek M., Dezortova M., Materka A., Lerski R. (eds.): Texture Analysis for Magnetic Resonance Imaging, pp. 107-113, Med4publishing, Prague. 2006.
• [31] Bhuiyan A., Nath B., Chua J., Kotagiri R.: Blood Vessel Segmentation from Color Retinal Images using Unsupervised Texture Classification. In: Proc. IEEE Int. Conf. Image Processing, vol. 5, pp. 521-524, 2007.