期刊
PATTERN RECOGNITION
卷 36, 期 10, 页码 2301-2315出版社
ELSEVIER SCI LTD
DOI: 10.1016/S0031-3203(03)00131-6
关键词
independent component analysis; image model; data dependent filter bank; texture segmentation; energy features
Independent component analysis (ICA) of textured images is presented as a computational technique for creating a new data dependent filter bank for use in texture segmentation. We show that the ICA filters are able to capture the inherent properties of textured images. The new filters are similar to Gabor filters, but seem to be richer in the sense that their frequency responses may be more complex. These properties enable us to use the ICA filter bank to create energy features for effective texture segmentation. Our experiments using multi-textured images show that the ICA filter bank yields similar or better segmentation results than the Gabor filter bank. (C) 2003 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.
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