Graph Cuts in Content-Based Image Classification and Retrieval with Relevance Feedback

  • Ning Zhang
  • Ling Guan
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4810)


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.


Graph Cuts Relevance Feedback Content-based Image Retrieval Radial Basis Function Maximum-flow/minimum-cut 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Ning Zhang
    • 1
  • Ling Guan
    • 1
  1. 1.Department of Electrical and Computer Enineering, Ryerson Uiversity, TorontoCanada

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