Support vector machine active learning for image retrieval
MULTIMEDIA '01 Proceedings of the ninth ACM international conference on Multimedia
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Cortina: a system for large-scale, content-based web image retrieval
Proceedings of the 12th annual ACM international conference on Multimedia
Multimodal concept-dependent active learning for image retrieval
Proceedings of the 12th annual ACM international conference on Multimedia
Hierarchical clustering of WWW image search results using visual, textual and link information
Proceedings of the 12th annual ACM international conference on Multimedia
Semantic knowledge extraction and annotation for web images
Proceedings of the 13th annual ACM international conference on Multimedia
Support vector machines for region-based image retrieval
ICME '03 Proceedings of the 2003 International Conference on Multimedia and Expo - Volume 1
Relevance feedback: a power tool for interactive content-based image retrieval
IEEE Transactions on Circuits and Systems for Video Technology
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There is a great deal of research conducted on hyperplane based query such as Support Vector Machine (SVM) in Content-based Image Retrieval(CBIR). However, the SVM-based CBIR always suffers from the problem of the imbalance of image data. Specifically, the number of negative samples (irrelevant images) is far more than that of the positive ones. To deal with this problem, we propose a new active learning approach to enhance the positive sample set in SVM-based Web image retrieval. In our method, instead of using complex parsing methods to analyze Web pages, two kinds of "lightweight" image features: the URL of the Web image and its visual features, which can be easily obtained, are applied to estimate the probability of the image being a potential positive sample. The experiments conducted on a test data set with more than 10,000 images from about 50 different Web sites demonstrate that compared with traditional methods, our approach improves the retrieval performance significantly.