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Unsupervised texture segmentation using Gabor filters
Authors:Anil K Jain  Farshid Farrokhnia
Institution:

? Department of Computer Science, Michigan State University, East Lansing, MI 48824, U.S.A.

? Innovision Corporation, 8000 Excelsior Drive, Madison, WI 53717, U.S.A.

Abstract:This paper presents a texture segmentation algorithm inspired by the multi-channel filtering theory for visual information processing in the early stages of human visual system. The channels are characterized by a bank of Gabor filters that nearly uniformly covers the spatial-frequency domain, and a systematic filter selection scheme is proposed, which is based on reconstruction of the input image from the filtered images. Texture features are obtained by subjecting each (selected) filtered image to a nonlinear transformation and computing a measure of “energy” in a window around each pixel. A square-error clustering algorithm is then used to integrate the feature images and produce a segmentation. A simple procedure to incorporate spatial information in the clustering process is proposed. A relative index is used to estimate the “true” number of texture categories.
Keywords:Texture segmentation  Multi-channel filtering  Gabor filters  Wavelet transform  Clustering  Clustering index
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