Image Retrieval: Feature Primitives, Feature Representation, and Relevance Feedback
نویسندگان
چکیده
In this paper we review the feature selection and representation techniques in CBIR systems, and propose 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. Multi-scale feature formation is proposed to support cross-resolution image matching. Sub-image feature extraction is applied for regional matching. Relevance feedback as an on-line learning mechanism is adopted for feature and tile selection and weighting during the retrieval. We discuss in detail the revised water-filling edge features, crossscale feature extraction and image matching, and relevance feedback on regional/tile-based matching.
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