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Optimum Gabor filter design and local binary patterns for texture segmentation
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DOI：10.1016/j.patrec.2007.12.001.png)
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摘要
En 中文
We present a novel approach to multi-texture image segmentation based on the formation of an effective texture feature vector. Texture sub-features are derived from the output of an optimized Gabor filter. The filter's parameters are selected by an immune genetic algorithm, which aims at maximizing the discrimination between the multi-textured regions. Next the texture features are integrated with a local binary pattern, to form an effective texture descriptor with low computational cost, which overcomes the weakness of the single frequency output component of the filter. Finally, a K-nearest neighbor classifier is used to effect the multi-texture segmentation. The integration of the optimum Gabor filter and local binary pattern methods provide a novel solution to the task. Experimental results demonstrate the effectiveness of the proposed approach. (C) 2008 Elsevier B.V. All rights reserved.
Keyword:
texture segmentation
Gabor filter
local binary pattern
K-nearest neighbor
immune genetic algorithm
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3.3
论文数:
8.0K
被引数:
1.6W
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