An Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Graph Cuts and Efficient N-D Image Segmentation
International Journal of Computer Vision
An interactive approach for CBIR using a network of radial basis functions
IEEE Transactions on Multimedia
Relevance feedback: a power tool for interactive content-based image retrieval
IEEE Transactions on Circuits and Systems for Video Technology
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Content-based image retrieval (CBIR) has suffered from the lack of linkage between low-level features and high-level semantics. Although relevance feedback (RF) CBIR provides a promising solution involving human interaction, certain query images poorly represented by low-level features still have unsatisfactory retrieval results. An innovative method has been proposed to increase the percentage of relevance of target image database by using graph cuts theory with the maximum-flow/minimum-cut algorithm and relevance feedback. As a result, the database is reformed by keeping relevant images while discarding irrelevant images. The relevance is increased and thus during following RF-CBIR process, previously poorly represented relevant images have higher probability to appear for selection. Better performance and retrieval results can thus be achieved.