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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Peng Zhang Jing Peng Buckles, B. |
| Copyright Year | 2006 |
| Description | Author affiliation: Dept. of Electr. Eng. & Comput. Sci., Tulane Univ., New Orleans, LA (Peng Zhang; Jing Peng; Buckles, B.) |
| Abstract | 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 |
| Sponsorship | IEEE CPS |
| Starting Page | 1138 |
| Ending Page | 1141 |
| File Size | 569590 |
| Page Count | 4 |
| File Format | |
| ISBN | 0769525210 |
| ISSN | 10514651 |
| DOI | 10.1109/ICPR.2006.753 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2006-08-20 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Filter bank Gabor filters Filtering Feature extraction Frequency domain analysis Kernel Support vector machines Support vector machine classification Image representation Optimization methods |
| Content Type | Text |
| Resource Type | Article |
| Subject | Computer Vision and Pattern Recognition |
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