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Content Provider | IEEE Xplore Digital Library |
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Author | ByoungChul Ko Hyeran Byun |
Copyright Year | 2002 |
Description | Author affiliation: Dept. of Comput. Sci., Yonsei Univ., South Korea (ByoungChul Ko; Hyeran Byun) |
Abstract | Among representative content-based image retrieval schemes, region-based retrieval has shown promise in retrieving similar images that exhibit considerable local variations. However, since humans are accustomed to relying on object-level concepts rather than low-level regions, robust and accurate object segmentation is an essential step. We propose a new multiple-region level image retrieval algorithm based on region-level image segmentation and its spatial relationship. To capture spatial similarity, we apply Hausdorff distance (HD) to our region-based image retrieval system, FRIP (finding region in the pictures). In contrast to other object or multiple region-based retrieval systems, we update classical HD to retrieve similar regions regardless of their spatial translation, insertion, and deletion. Furthermore, we incorporate relevance feedback to reflect the user's high-level query and subjectivity to the system and to compensate for performance degradation due to imperfect image segmentation. The efficacy of our method is validated using a set of 3000 images from Corel-photo CD. |
Starting Page | 196 |
Ending Page | 199 |
File Size | 322774 |
Page Count | 4 |
File Format | |
ISBN | 076951695X |
ISSN | 10514651 |
DOI | 10.1109/ICPR.2002.1044649 |
Language | English |
Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Publisher Date | 2002-08-11 |
Publisher Place | Canada |
Access Restriction | Subscribed |
Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Subject Keyword | Image retrieval Image segmentation Robustness Content based retrieval Humans Object segmentation High definition video Feedback Degradation Information retrieval |
Content Type | Text |
Resource Type | Article |
Subject | Computer Vision and Pattern Recognition |
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