Our starting point is gradient indexing, the characterization of texture by a feature vector that comprises a histogram derived from the image gradient field. We investigate the use of gradient indexing for texture recognition and image retrieval. We find that gradient indexing is a robust measure with respect to the number of bins and to the choice of the gradient operator. We also find that the gradient direction and magnitude are equally effective in recognizing different textures. Furthermore, a variant of gradient indexing called local activity spectrum is proposed and shown to have improved performance. Local activity spectrum is employed in an image retrieval system as the texture statistic. The retrieval system is based on a segmentation technique employing a distance measure called Sum of Minimum Distance. This system enables content-based retrieval of database images from templates of arbitrary size.
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