Texture classification is a fundamental and yet difficult task in machine vision and image processing. In recent years, more and more researchers' attention has been drawn to the sparse representation-based classification (SRC) method and its corresponding dictionaries designing in pattern recognition community, due to its high recognition rate, robustness to corruption and occlusion, and little dependence on the features, etc. In this paper, we present a discriminative dictionary learning approach, and apply it to the sparse representation based classification framework for image texture representation and classification. The experimental results conducted on different testing data demonstrate the promise of our new approach when compared with the previous algorithms.

Date Published: 16 April 2014
PDF: 5 pages
Proc. SPIE 9159, Sixth International Conference on Digital Image Processing (ICDIP 2014), 91590S (16 April 2014); doi: 10.1117/12.2064233

Published in SPIE Proceedings Vol. 9159:
Sixth International Conference on Digital Image Processing (ICDIP 2014)
Charles M. Falco; Chin-Chen Chang; Xudong Jiang, Editor(s)