Multiple boosting SVM active learning for image retrieval

Wei Jiang, Guihua Er, Qionghai Dai

Abstract: Content-based image retrieval can be viewed as a classification problem, and the small sample size leaning difficulty makes it difficult for most CBIR classifiers to get satisfactory performance. In this paper, using the SVM classifier as the component classifier, the method of ensemble of classifiers is incorporated into the relevance feedback process to alleviate this problem from two aspects: (1) within each feedback round, multiple parallel component classifiers are constructed, one over one feature subspace individually, and then are merged together to get an ensemble classifier; (2) during feedback rounds, a boosting method is incorporated to sequentially combine the component classifiers over each feature subspace respectively, which further improves the classification result. Experiments over 5000 images show that the proposed method can improve the retrieval performance consistently, without loss of efficiency.