Dynamic Feature Space Selection in Relevance Feedback Using Support Vector Machines

The selection of relevant features plays a critical role in relevance feedback for content-based image retrieval. In this paper, we propose an approach for dynamically selecting the most relevant feature space in relevance feedback. During the feedback process, an SVM classifier is constructed in each feature space, and its generalization error is estimated. The feature space with the smallest generalization error is chosen for the next round of retrieval. Several kinds of estimators are discussed. We demonstrate experimentally that the prediction of the generalization error of SVM classifier is effective in relevant feature space selection for content-based image retrieval.