International Journal of Computer Vision
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
Comparing discriminating transformations and SVM for learning during multimedia retrieval
MULTIMEDIA '01 Proceedings of the ninth ACM international conference on Multimedia
SIMPLIcity: Semantics-Sensitive Integrated Matching for Picture LIbraries
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
MindReader: Querying Databases Through Multiple Examples
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Multi-class relevance feedback content-based image retrieval
Computer Vision and Image Understanding
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
An introduction to kernel-based learning algorithms
IEEE Transactions on Neural Networks
Learning similarity measure for natural image retrieval with relevance feedback
IEEE Transactions on Neural Networks
Negative Samples Analysis in Relevance Feedback
IEEE Transactions on Knowledge and Data Engineering
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Relevance feedback (RF) is an important tool to improve the performance of content-based image retrieval system. Support vector machine (SVM) based RF is popular because it can generalize better than most other classifiers. However, directly using SVM in RF may not be appropriate, since SVM treats the positive and negative feedbacks equally. Given the different properties of positive samples and negative samples in RF, they should be treated differently. Considering this, we propose an orthogonal complement components analysis (OCCA) combined with SVM in this paper. We then generalize the OCCA to Hilbert space and define the kernel empirical OCCA (KEOCCA). Through experiments on a Corel Photo database with 17,800 images, we demonstrate that the proposed method can significantly improve the performance of conventional SVM-based RF.