Content-aware search of multimedia data in ad hoc networks
MSWiM '05 Proceedings of the 8th ACM international symposium on Modeling, analysis and simulation of wireless and mobile systems
Content-based image retrieval: approaches and trends of the new age
Proceedings of the 7th ACM SIGMM international workshop on Multimedia information retrieval
Image retrieval: Ideas, influences, and trends of the new age
ACM Computing Surveys (CSUR)
A statistical image retrieval method using color invariant
CIRA'09 Proceedings of the 8th IEEE international conference on Computational intelligence in robotics and automation
An endmember-based distance for content based hyperspectral image retrieval
Pattern Recognition
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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. While many interestingimage segmentation techniques have been proposed, their performance in practical applications remains limited. Thus, integrating related regions into meaningful objects becomes a promising alternative. In this paper, we propose a new multiple-region level image retrieval algorithm based on region-level image segmentation and its spatial relationship. To capturespatial 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.