Texture segmentation by clustering of Gabor feature vectors

Approaches the texture segmentation problem by clustering feature vectors created from a Gabor transform data block. Given an N×N image, the authors compute 24 Gabor transforms using Gabor kernels with six orientations and four sizes. This results in a Gabor data block composed of N² feature vectors of length 24. The feature vectors are then grouped based on their distribution in the high-dimensional feature space. The authors hypothesize that the pixels in a given group have similar characteristics, and thus are part of the same texture. Experimental results for segmenting a synthetic railroad track image were encouraging; a clear-cut segmentation of the image was obtained. By clustering Gabor features, the authors were able to segment an image into regions of uniform texture without prior knowledge of the types of texture, or the frequency and orientation characteristics of these textures. The clustering algorithm is a modified Kohonen self-organizing feature map