Abstract
In this paper feature selection and representation techniques in CBIR systems are reviewed and interpreted in a unified feature representation paradigm. We revise our previously proposed water-filling edge features with newly proposed primitives and present them using this unified feature formation paradigm. Experiments and comparisons are performed to illustrate the characteristics of the new features. Also proposed is sub-image feature extraction for regional matching. Relevance feedback as an on-line learning mechanism is adopted for feature and tile selection and weighting during the retrieval.
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