Classifying 3D Real-World Texture Images by Combining Maximum Response 8, 4th Order of Auto Correlation and Colortons
Aram Kawewong and Osamu Hasegawa
Imaging Science and Engineering Laboratory, Tokyo Institute of Technology, 4259 Nagatsuta, Midori-ku, Yokohama 226-8503, Japan
A novel three-dimensional (3D) texture classification approach is proposed. It combines the use of Maximum Response 8 Filters (MR8) and 4th order of Auto Correlation Filters (Autocor). These features are combined using Between-Class Error (BCE) information that is learned in a Pre-Testing Stage. Variance (MR8) and invariance (Autocor) of rotation present advantages and disadvantages. Our approach is intended to eliminate those drawbacks and combine favorable features. Our approach performs two single classification methods individually and in parallel. It selects output from the method that obtains the fewest BCEs. Additionally, color from the texture images was considered as an effective additional feature for texture classification. The overall performance of our approach was drastically improved by the addition of that feature. The efficiency of our approach was evaluated by employing over 5500 texture images that correspond to 61 real-world surface samples from the Columbia-Utrecht Reflectance and Texture (CUReT) database . A classification percentage of 97.77% for 61 classes was achieved when Autocor  and MR8  were combined. Superior performance was achieved by adding new color feature data: Colortons. Using them, the classification rate was 98.94% for 61 classes.
-  O. J. Cula and K. J. Dana, “3D Texture Recognition Using Bidirectional Feature Histograms,” International Journal of Computer Vision, Kluwer Academic Publisher, 59 (1), pp. 33-60, 2004.
-  K. J. Dana, B. van Ginneken, S. K. Nayar, and J. J. Koenderink, “Reflectance and Texture of Real-World Surfaces,” ACM Trans. Graphics, 18 (1), pp. 1-34, 1999.
-  R. O. Duda, P. E. Hart, and D. G. Stork, “Pattern Classification,” John Wiley and Sons, 2nd edition, 2001.
-  T. K. Ho, J. J. Hull, and S. N. Srihari, “Decision Combination in Multiple Classifier Systems,” IEEE Transactions on Pattern Analysis and Machine Intelligence, 16 (1), pp. 66-75, January 1994.
-  J. Kittler, M. Hatef, R. P. W. Duin, and J. Matas, “On Combining Classifiers,” IEEE Transaction on Pattern Analysis and Machine Intelligence, 20 (3), pp. 226-239.
-  L. Lepisto, I. Kunttu, J. Autio, and A. Visa, “Classification of Non-Homogenous Texture Images by Combining Classifiers,” Proceedings of IEEE International Conference on Image Processing, Vol.1, pp. 981-984, 2003.
-  T. Leung and J. Malik, “Representing and Recognizing the Visual Appearance of Materials Using Three-Dimensional Textons,” International Journal of Computer Vision, Kluwer Academic Publisher, 43 (1), pp. 29-44, 2001.
-  O. Melnik, Y. Vardi, and C. Zhang, “Mixed Group Ranks: Preference and Confidence in Classifier Combination,” IEEE Transactions on Pattern Analysis and Machine Intelligence, 23 (8), pp. 973-981, August 2004.
-  C. D. Stefano, C. D. Cioppa, A. Marcelli, and A. Daeiimi, “An Adaptive Weighted Majority Vote Rule for Combining Multiple Classifiers,” Proceedings of 16th International Conference on Pattern Recognition, Canada, Vol.2, 2002.
-  C. D. Stefano, A. D. Cioppa, and A. Marcelli, “Exploiting Reliability for Dynamic Selection of Classifiers by means of Genetic Algorithms,” Proceedings of the 17th International Conference on Document Analysis and Recognition, Scotland, 2003.
-  P. Seun and G. Healy, “The Analysis and Recognition of Real-World Textures in Three Dimensions,” IEEE Transactions on Pattern Analysis and Machine Intelligence, 22 (5), pp. 491-503, May 2000.
-  T. Toyoda and O. Hasegawa, “Systematic Feature Extraction Using Mask Patterns for High Precision Image Analysis,” Joint 2nd International Conference on Soft Computing and Intelligent Systems and 5th International Symposium on Advanced Intelligent Systems (SCIS & ISIS), Korea, CD-ROM THE-4-3, 2004.
-  M. Varma and A. Zisserman, “A Statistical Approach to Texture Classification from Single Images,” International Journal of Computer Vision, Kluwer Academic Publisher, 62 (1-2), pp. 61-68, April 2005.
-  M. Pietikäinen, T. Nurmela, T. Mäenpää, and M. Turtinen, “View-Based Recognition of Real-World Textures,” Pattern Recognition, Vol.37, pp. 313-323, 2004.
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