Support vector machine active learning for image retrieval
MULTIMEDIA '01 Proceedings of the ninth ACM international conference on Multimedia
Random Sampling SVM Based Soft Query Expansion for Image Retrieval
ICIG '07 Proceedings of the Fourth International Conference on Image and Graphics
SVM-based active feedback in image retrieval using clustering and unlabeled data
Pattern Recognition
Performance evaluation of relevance feedback methods
CIVR '08 Proceedings of the 2008 international conference on Content-based image and video retrieval
Semisupervised SVM batch mode active learning with applications to image retrieval
ACM Transactions on Information Systems (TOIS)
Biased discriminant euclidean embedding for content-based image retrieval
IEEE Transactions on Image Processing
Image retrieval based on incremental subspace learning
Pattern Recognition
Weighting visual features with pseudo relevance feedback for CBIR
Proceedings of the ACM International Conference on Image and Video Retrieval
Bregman Divergence-Based Regularization for Transfer Subspace Learning
IEEE Transactions on Knowledge and Data Engineering
Active learning in multimedia annotation and retrieval: A survey
ACM Transactions on Intelligent Systems and Technology (TIST)
A Lazy Processing Approach to User Relevance Feedback for Content-Based Image Retrieval
ISM '10 Proceedings of the 2010 IEEE International Symposium on Multimedia
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part III
IEEE Transactions on Image Processing
Relevance feedback: a power tool for interactive content-based image retrieval
IEEE Transactions on Circuits and Systems for Video Technology
Which Components are Important for Interactive Image Searching?
IEEE Transactions on Circuits and Systems for Video Technology
Semantic Subspace Projection and Its Applications in Image Retrieval
IEEE Transactions on Circuits and Systems for Video Technology
Assistive tagging: A survey of multimedia tagging with human-computer joint exploration
ACM Computing Surveys (CSUR)
A Survey on Visual Content-Based Video Indexing and Retrieval
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
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In content-based image retrieval (CBIR), the gap between low-level visual features and high-level semantic meanings usually leads to poor performance, and relevance feedback (RF) is an effective method to bridge this gap and to scale up the performance in CBIR systems. In recent years, the support vector machine (SVM) based relevance feedbacks have been popular because they can outperform many other classifiers when the size of the training set is small, but they are often very complex and some unsatisfactory relevance of results occur frequently. To overcome the above limitations, we propose a SVM relevance feedback CBIR algorithm based on feature reconstruction, in which the covariance matrix based kernel empirical orthogonal complement component analysis is utilized. Firstly, the original input image space is projected nonlinearly onto a high-dimensional feature space by using nonlinear analysis approaches. Secondly, the covariance matrix of the positive feedback images are calculated, and the kernel empirical orthogonal complement components of the covariance matrix are also calculated. Thirdly, the new features of positive feedback images, negative feedback images, and all the remaining images are reconstructed by utilizing the kernel empirical orthogonal complement components of positive feedback images. Finally, a SVM classifier is constructed and all the images are resorted based on the new reconstructed image feature. Experiments on large databases show that the proposed algorithms are significantly more effective than the state-of-the-art approaches.