Content-Based Image Retrieval at the End of the Early Years
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
Knowledge discovery in multimedia repositories: the role of metadata
MMACTE'05 Proceedings of the 7th WSEAS International Conference on Mathematical Methods and Computational Techniques In Electrical Engineering
Non-parametric kernel ranking approach for social image retrieval
Proceedings of the ACM International Conference on Image and Video Retrieval
Distance metric learning from uncertain side information for automated photo tagging
ACM Transactions on Intelligent Systems and Technology (TIST)
A multiple instance approach for keyword-based retrieval in un-annotated image database
MMM'10 Proceedings of the 16th international conference on Advances in Multimedia Modeling
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Without textual descriptions or label information of images, searching semantic concepts in image databases is still a very challenging task. While automatic annotation techniques are yet along way off, we can seek other alternative techniques to solve this difficult issue. In this paper, we propose to learn Web images for searching the semantic concepts in large image databases. To formulate effective algorithms, we suggest to engage the support vector machines for attacking the problem. We evaluate our algorithm in a large image database and demonstrate the preliminary yet promising results.