TEXTURE CATEGORIZATION WITH BIOLOGICALLY INSPIRED FEATURES AND RANDOM FORESTS
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LITERATURE REVIEWAbstract
Texture classification is used extensively in computer vision application and images analysis. The aim of this paper was to use biologically inspired mechanisms for features extraction and random forests as a classifier to enhance the texture classification. These mechanisms wereimplemented by multi channels Gabor filter andmulti-scaledifference of Gaussian, which were combined efficiently using local binary pattern histograms. These histograms were computedinnone overlappedwindow and classified by ensemble random forests. The experiments results demonstrate that the proposed method improves classification rates. The proposed method achieves higherclassification ratescompared to other methods
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