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Recent progress in multimedia database systems has resulted in solutions for integrating and managing a variety of multimedia formats that include images, video, audio, and text [1]. Advances in automatic feature extraction and image-content analysis have enabled the development of new functionalities for searching, filtering, and accessing images based on perceptual features such as color [2,3], texture [4,5], shape [6], and spatial composition [7]. | Image Databases Search and Retrieval of Digital Imagery Edited by Vittorio Castelli Lawrence D. Bergman Copyright 2002 John Wiley Sons Inc. ISBNs 0-471-32116-8 Hardback 0-471-22463-4 Electronic 11 Color for Image Retrieval JOHN R. SMITH IBM . Watson Research Center Hawthorne New York INTRODUCTION Recent progress in multimedia database systems has resulted in solutions for integrating and managing a variety of multimedia formats that include images video audio and text 1 . Advances in automatic feature extraction and image-content analysis have enabled the development of new functionalities for searching filtering and accessing images based on perceptual features such as color 2 3 texture 4 5 shape 6 and spatial composition 7 . The content-based query paradigm which allows similarity searching based on visual features addresses the obstacles to access color image databases that result from the insufficiency of key word or text-based annotations to completely consistently and objectively describe the content of images. Although perceptual features such as color distributions and color layout often provide a poor characterization of the actual semantic content of the images content-based query appears to be effective for indexing and rapidly accessing images based on the similarity of visual features. Content-Based Query Systems The seminal work on content-based query of image databases was carried out in the IBM query by image content QBIC project 2 8 . The QBIC project explored methods for searching for images based on the similarity of global image features of color texture and shape. The QBIC project developed a novel method of prefiltering of queries that greatly reduces the number of target images searched in similarity queries 9 . The MIT Photobook project extended some of the early methods of content-based query by developing descriptors that provide effective matching as well as the ability to reconstruct the images and their features from the