CLUSTERING-BASED SUBSPACE SVM ENSEMBLE FOR RELEVANCE FEEDBACK LEARNING

This paper presents a subspace SVM ensemble algorithm for adaptive relevance feedback (RF) learning. Our method deals with the case that user's relevance feedback examples are usually insufficient and overlapped together in feature space, which decreases the learning effectiveness of RF classifiers. To enhance classification efficiency in such case, multiple SVMs are learned by clustering-based training set partition, each of which fits its cluster-specific sample distribution and gives labeling regressions to test samples that fall within this cluster. To adapt features to sample distribution within each cluster, AdaBoost feature selection is conducted onto pyramid Haar of H&I bands in HSI space. In AdaBoost, we evaluate the feature discriminative ability by an entropy-based uncertainty criterion, based on which an Eigen feature subspace is constructed in cluster-specific SVM training. Finally, regression results of multiple SVMs are probabilistic assembled to give the final labeling prediction for test image. We compare our cluster-based cascade SVMs (CSS) RF method in COREL 5,000 database with: 1. Single SVM; 2. Active Learning SVM [5]; 3. Bootstrap Sampling SVM [7]. The superior experimental results demonstrate the efficiency of our algorithm.