Dictionary learning in texture classification

Authors:
Mehrdad J. Gangeh;Ali Ghodsi;Mohamed S. Kamel
Affiliations:
Pattern Analysis and Machine Intelligence, Lab, Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, Ontario, Canada;Department of Statistics and Actuarial Science, University of Waterloo, Waterloo, Ontario, Canada;Pattern Analysis and Machine Intelligence, Lab, Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, Ontario, Canada
Venue:
ICIAR'11 Proceedings of the 8th international conference on Image analysis and recognition - Volume Part I
Year:
2011

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Abstract

Texture analysis is used in numerous applications in various fields. There have been many different approaches/techniques in the literature for texture analysis among which the texton-based approach that computes the primitive elements representing textures using k-means algorithm has shown great success. Recently, dictionary learning and sparse coding has provided state-of-the-art results in various applications. With recent advances in computing the dictionary and sparse coefficients using fast algorithms, it is possible to use these techniques to learn the primitive elements and histogram of them to represent textures. In this paper, online learning is used as fast implementation of sparse coding for texture classification. The results show similar to or better performance than texton based approach on CUReT database despite of computation of dictionary without taking into account the class labels.