Texture recognition and image retrieval using gradient indexing

Our starting point is gradient indexing, the characterization of texture by a feature vector that comprises a histogram derived from the image gradien t field. We investigate the use of gradient indexing for texture recognitio n and image retrieval. We find that gradient indexing is a robust measure w ith respect to the number of bins and to the choice of the gradient operato r. We also find that the gradient direction and magnitude are equally effec tive in recognizing different textures. Furthermore, a variant of gradient indexing called local activity spectrum is proposed and shown to have impro ved performance. Local activity spectrum is employed in an image retrieval system as the texture statistic. The retrieval system is based on a segment ation technique employing a distance measure called Sum of Minimum Distance . This system enables content-based retrieval of database images from templ ates of arbitrary size. (C) 2000 Academic Press.