WALRUS: a similarity retrieval algorithm for image databases
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Query refinement for multimedia similarity retrieval in MARS
MULTIMEDIA '99 Proceedings of the seventh ACM international conference on Multimedia (Part 1)
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
IRM: integrated region matching for image retrieval
MULTIMEDIA '00 Proceedings of the eighth ACM international conference on Multimedia
MindReader: Querying Databases Through Multiple Examples
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Relevance feedback using adaptive clustering for region based image similarity retrieval
PRICAI'06 Proceedings of the 9th Pacific Rim international conference on Artificial intelligence
Estimating the number of clusters using multivariate location test statistics
FSKD'06 Proceedings of the Third international conference on Fuzzy Systems and Knowledge Discovery
Relevance feedback in region-based image retrieval
IEEE Transactions on Circuits and Systems for Video Technology
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In this paper, a new hybrid weighting method, which learns region importance from the region size and the spatial location of regions in an image, is introduced to re-weight regions optimally and improve the performance of the region-based search system on the Web. Relevant images marked by an user may exhibit very different visual characteristics so that they may be scattered in several clusters in the feature space, since there exists the semantic gap between the low level feature and the high level semantics in user's mind. Our main goal is to find semantically related clusters and their weights to narrow down this semantic gap. To do this, The hybrid region weighting method, which refines the weights of region-clusters through relevance feedback, determines the importance of regions according to the region size and spatial location information of regions in an image. Experimental results demonstrate the efficiency and the effectiveness of the proposed weighting method in comparison with the area percentage method and the region frequency weighted by inverse image frequency method, respectively.