The nature of statistical learning theory
The nature of statistical learning theory
Texture Features for Browsing and Retrieval of Image Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
Photobook: content-based manipulation of image databases
International Journal of Computer Vision
Content-Based Image Retrieval at the End of the Early Years
IEEE Transactions on Pattern Analysis and Machine Intelligence
MULTIMEDIA '00 Proceedings of the eighth ACM international conference on Multimedia
Support vector machine active learning for image retrieval
MULTIMEDIA '01 Proceedings of the ninth ACM international conference on Multimedia
Support vector machine active learning with applications to text classification
The Journal of Machine Learning Research
Image Retrieval from the World Wide Web: Issues, Techniques, and Systems
ACM Computing Surveys (CSUR)
Mean version space: a new active learning method for content-based image retrieval
Proceedings of the 6th ACM SIGMM international workshop on Multimedia information retrieval
Incremental semi-supervised subspace learning for image retrieval
Proceedings of the 12th annual ACM international conference on Multimedia
A novel log-based relevance feedback technique in content-based 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
Estimating the Support of a High-Dimensional Distribution
Neural Computation
Neural Computation
PicToSeek: combining color and shape invariant features for image retrieval
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
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Eliminating semantic gaps is important for image retrieving and annotating in content based image retrieval (CBIR), especially under web context. In this paper, a relevance feedback learning approach is proposed for web image retrieval, by using soft support vector machine (Soft-SVM). An active learning process is introduced to Soft-SVM based on a novel sampling rule. The algorithm extends the conventional SVM by using a loose factor to make the decision plane partial to the uncertain data and reduce the learning risk. To minimize the overall cost, a new feedback model and an acceleration scheme are applied to the learning system for reducing the cost of data collection and improving the classifier accuracy. The algorithm can improve the performance of image retrieving effectively.