Iterative refinement by relevance feedback in content-based digital image retrieval
MULTIMEDIA '98 Proceedings of the sixth ACM international conference on Multimedia
Support vector machine active learning for image retrieval
MULTIMEDIA '01 Proceedings of the ninth ACM international conference on Multimedia
SIMPLIcity: Semantics-Sensitive Integrated Matching for Picture LIbraries
IEEE Transactions on Pattern Analysis and Machine Intelligence
Blobworld: Image Segmentation Using Expectation-Maximization and Its Application to Image Querying
IEEE Transactions on Pattern Analysis and Machine Intelligence
Normalized Cuts and Image Segmentation
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Segmentation Given Partial Grouping Constraints
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
LOCUS: Learning Object Classes with Unsupervised Segmentation
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Cosegmentation of Image Pairs by Histogram Matching - Incorporating a Global Constraint into MRFs
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Relevance feedback: a power tool for interactive content-based image retrieval
IEEE Transactions on Circuits and Systems for Video Technology
Relevance feedback in region-based image retrieval
IEEE Transactions on Circuits and Systems for Video Technology
Editorial: Interactive imaging and vision-Ideas, algorithms and applications
Pattern Recognition
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Relevance feedback is an important mechanism for narrowing the semantic gap in content-based image retrieval and the process involves the user labeling positive and negative images. Very often, it is some specific objects or regions in the positive feedback images that the user is really interested in rather than the entire image. This paper presents a hierarchical graphical model for automatically extracting objects and regions that the user is interested in from the positive images which in turn are used to derive features that better reflect the user's feedback intentions for improving interactive image retrieval. The novel hierarchical graphical model embeds image formation prior, user intention prior and statistical prior in its edges and uses a max-flow/min-cut method to simultaneously segment all positive feedback images into user interested and user uninterested regions. An important innovation of the graphical model is the introduction of a layer of visual appearance prototype nodes to incorporate user intention and form bridges linking similar objects in different images. This architecture not only makes it possible to use all feedback images to obtain more robust user intention prior thus improving the object segmentation results and in turn enhancing the retrieval performance, but also greatly reduces the complexity of the graph and the computational cost. Experimental results are presented to demonstrate the effectiveness of the new method.