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IRM: Integrated Region Matching for Image Retrieval (2000)
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| Citations: | 101 - 14 self |
BibTeX
@INPROCEEDINGS{Li00irm:integrated,
author = {Jia Li and James Z. Wang and Gio Wiederhold},
title = {IRM: Integrated Region Matching for Image Retrieval},
booktitle = {},
year = {2000},
pages = {147--156}
}
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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 regionbased 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 ...
Keyphrases
region matching image retrieval inaccurate segmentation individual region region-based image retrieval system novel similarity measure region-matching scheme regionbased image similarity comparison particular region general-purpose image simple querying interface overall similarity approach irm measure overall similarity experimental simplicity image retrieval system present irm location property active research area targeted image retrieval system region segmentation content-based image retrieval
