Capturing high-level image concepts via affinity relationships in image database retrieval
Multimedia Tools and Applications
A survey of content-based image retrieval with high-level semantics
Pattern Recognition
Image retrieval system using R-tree self-organizing map
Data & Knowledge Engineering
Semantic clustering for region-based image retrieval
Journal of Visual Communication and Image Representation
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Computers & Mathematics with Applications
Aircraft identification by moment invariants
IEEE Transactions on Computers
Similarity searching in image retrieval with statistical distance measures and supervised learning
ICAPR'05 Proceedings of the Third international conference on Advances in Pattern Recognition - Volume Part I
IEEE Transactions on Information Technology in Biomedicine
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An image retrieval system, which reduces the semantic gap between the low level image features and the high level semantic concepts, using the semantic cluster matrix (SCM) and adaptive learning during testing, is proposed. This mechanism retrieves semantically relevant images for both trained and untrained image categories. The SCM groups the new categories into semantic clusters, records the cluster's semantic information, gets the relevance feedback (RF) from the user, and records this in the SCM. Thus, the SCM adaptively learns about the new categories during the testing time, and is able to retrieve semantically relevant images for untrained categories also. Experiments were conducted using the Caltech image dataset consisting of 101 categories of images. The obtained results demonstrate that the proposed approach achieves good performance in terms of retrieval accuracy.