IRM: Integrated Region Matching for Image Retrieval

Jia Li, James Z. Wang, Gio Wiederhold
Stanford University, Stanford, CA 94305
Abstract:

Content-based image retrieval using region segmentation has been an active research area. We present IRM (Integrated Region Matching), a novel similarity measure for region-based image similarity comparison. The targeted image retrieval systems represent an image by a set of regions, roughly corresponding to objects, which are characterized by features reflecting color, texture, shape, and location properties. The IRM measure for evaluating overall similarity between images incorporates properties of all the regions in the images by a region-matching scheme. Compared with retrieval based on individual regions, the overall similarity approach reduces the influence of inaccurate segmentation, helps to clarify the semantics of a particular region, and enables a simple querying interface for region-based image retrieval systems. The IRM has been implemented as a part of our experimental SIMPLIcity image retrieval system. The application to a database of about 200,000 general-purpose images shows exceptional robustness to image alterations such as intensity variation, sharpness variation, color distortions, shape distortions, cropping, shifting, and rotation. Compared with several existing systems, our system in general achieves more accurate retrieval at higher speed.


Full Paper in Color
(PDF, 5MB)
(HTML)

On-line Demo


Citation: Jia Li, James Z. Wang and Gio Wiederhold, ``IRM: Integrated Region Matching for Image Retrieval,'' Proc. ACM Multimedia, pp. 147-156, Los Angeles, CA, ACM, October 2000.

Copyright 2000 ACM. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works, must be obtained from the ACM.

Last Modified: July 10 2000
2000