We work towards a content-based image retrieval system, where queries can be image-like objects. At entry time, each image is processed to yield a large number of indices into its windows. A window is a square in a fixed quad-tree decomposition of the image, and an index is a fixed-size vector, called a descriptor, similar to the periodograms used in spectral estimation. The fixed decomposition of images was prompted by the need for fast processing, but leads to windows that often straddle image regions with different textural contents, making indices less effective. In this paper, we investigate different definitions of spectral distance which we plan to use to classify windows according to their texture content.
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