Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Extraction of feature subspaces for content-based retrieval using relevance feedback
MULTIMEDIA '01 Proceedings of the ninth ACM international conference on Multimedia
Interactive content-based image retrieval using relevance feedback
Computer Vision and Image Understanding
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Learning query-class dependent weights in automatic video retrieval
Proceedings of the 12th annual ACM international conference on Multimedia
A geometric interpretation of r-precision and its correlation with average precision
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Spatial Weighting for Bag-of-Features
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
A survey of content-based image retrieval with high-level semantics
Pattern Recognition
Image retrieval: Ideas, influences, and trends of the new age
ACM Computing Surveys (CSUR)
Pagerank for product image search
Proceedings of the 17th international conference on World Wide Web
CrowdReranking: exploring multiple search engines for visual search reranking
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Unsupervised Object Discovery: A Comparison
International Journal of Computer Vision
Object-based image retrieval using the statistical structure of images
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Object image retrieval by shape content in complex scenes using geometric constraints
IbPRIA'05 Proceedings of the Second Iberian conference on Pattern Recognition and Image Analysis - Volume Part I
Improving 3D similarity search by enhancing and combining 3D descriptors
Multimedia Tools and Applications
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This paper proposes an automatic visual feature weighting method to enhance content-based image retrieval (CBIR). In particular, the proposed method is able to capture user's search intention by identifying the important visual features located at region of interest. Given a query image, the importances of visual features are automatically weighted by a random walk algorithm from a feature association graph, whose association strength is estimated by a localized visual word co-occurrence count among a set of pseudo relevance feedbacks. The visual word here is defined with a bag-of-features model whose visual feature vocabulary is generated by a k-means clustering algorithm. For quantitative evaluation, we implement a prototype CBIR system with weighted visual features (WVF). Extensive experiments on CalTech-101 dataset demonstrate the efficiency and effectiveness of WVF for CBIR.