The nature of statistical learning theory
The nature of statistical learning theory
Support vector machine active learning for image retrieval
MULTIMEDIA '01 Proceedings of the ninth ACM international conference on Multimedia
Content-based multimedia information retrieval: State of the art and challenges
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
IEEE Transactions on Pattern Analysis and Machine Intelligence
An active feedback framework for image retrieval
Pattern Recognition Letters
Image retrieval: Ideas, influences, and trends of the new age
ACM Computing Surveys (CSUR)
Features for image retrieval: an experimental comparison
Information Retrieval
SVM-based active feedback in image retrieval using clustering and unlabeled data
Pattern Recognition
Ensemble one-class support vector machines for content-based image retrieval
Expert Systems with Applications: An International Journal
IEEE Transactions on Image Processing
A unified relevance feedback framework for web image retrieval
IEEE Transactions on Image Processing
Spoken Language Recognition Using Ensemble Classifiers
IEEE Transactions on Audio, Speech, and Language Processing
IEEE Transactions on Information Technology in Biomedicine
Active Learning Methods for Interactive Image Retrieval
IEEE Transactions on Image Processing
Computer Vision and Image Understanding
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In content-based image retrieval (CBIR), the support vector machine (SVM) based relevance feedback is studied extensively to narrow the gap between low-level image feature and high-level semantic concept. Despite the success, for conventional SVM relevance feedback, the retrieval performance is actually worse when the number of labeled positive feedback samples is small. To overcome this limitation, a SVM classifier combination for relevance feedback content-based image retrieval using expectation-maximization (EM) parameter estimation is proposed. Firstly, we introduce the asymmetric bagging SVM to improve the stability of SVM classifiers and the balance in the training. Then, the random subspace SVM is used to overcome the overfitting problem. Finally, we combine the asymmetric bagging SVM and the random subspace SVM using EM parameter estimation to form an integrated SVM as a hypothesized solution to the overall image retrieval problem, which can further improve the relevance feedback performance. Experiments on large databases show that the proposed algorithms are significantly more effective than the state-of-the-art approaches.