TY - GEN
T1 - Learning optimal filter representation for texture classification
AU - Peng, Zhang
AU - Jing, Peng
AU - Buckles, Bill
PY - 2006
Y1 - 2006
N2 - Crucial to texture classification are texture features and classifiers that operate on the features. There are several approaches to computing texture features. Of particular interest is multichannel filtering because of its simplicity. Multichannel filtering works by decomposing the frequency domain of an image, resulting in a bank of filtered feature images. Many techniques have been proposed to optimize multichannel filtering. However, the optimization is with respect to image representation, thus giving no guarantee for texture classification. This paper proposes a novel technique for learning optimal filters for texture classification. We use regularization techniques such as support vector machines (SVMs) to learn multichannel filters. Since filter training in our approach is naturally tied to classifier training, the resulting filters are optimized for classification. Experimental results validate the efficacy of our proposed technique.
AB - Crucial to texture classification are texture features and classifiers that operate on the features. There are several approaches to computing texture features. Of particular interest is multichannel filtering because of its simplicity. Multichannel filtering works by decomposing the frequency domain of an image, resulting in a bank of filtered feature images. Many techniques have been proposed to optimize multichannel filtering. However, the optimization is with respect to image representation, thus giving no guarantee for texture classification. This paper proposes a novel technique for learning optimal filters for texture classification. We use regularization techniques such as support vector machines (SVMs) to learn multichannel filters. Since filter training in our approach is naturally tied to classifier training, the resulting filters are optimized for classification. Experimental results validate the efficacy of our proposed technique.
UR - http://www.scopus.com/inward/record.url?scp=34047192530&partnerID=8YFLogxK
U2 - 10.1109/ICPR.2006.753
DO - 10.1109/ICPR.2006.753
M3 - Conference contribution
AN - SCOPUS:34047192530
SN - 0769525210
SN - 9780769525211
T3 - Proceedings - International Conference on Pattern Recognition
SP - 1138
EP - 1141
BT - Proceedings - 18th International Conference on Pattern Recognition, ICPR 2006
T2 - 18th International Conference on Pattern Recognition, ICPR 2006
Y2 - 20 August 2006 through 24 August 2006
ER -