Content-based image retrieval by viewpoint-invariant color indexing

Abstract

We aim at content-based image retrieval by color information indexing robust against varying imaging conditions. To this end, we propose a new set of color features robust to a large change in viewpoint, object geometry and illumination. From the proposed set, various color features are selected to construct color pattern-cards for each image. Matching measurers are defined, expressing similarity between color pattern-cards, robust to a substantial amount of object occlusion and cluttering. Based on the color pattern-cards and matching measures, a hashing scheme is presented offering constant run-time image retrieval independent of the number of images in the image database. To evaluate accuracy of the image retrieval scheme, experiments have been conducted on a database consisting of 500 images taken from multicolored man-made objects in real world scenes. The results show that high image retrieval accuracy is achieved. Also, robustness is demonstrated against a change in viewing position, partial occlusion, and a substantial amount of object cluttering. Finally, the image retrieval scheme is integrated into the PicToSeek system on-line at http://www.wins.uva.nl/research/isis/PicToSeek/ for searching images on the World Wide Web.