Stochastic exploration and active learning for image retrieval

This paper deals with content-based image retrieval. When the user is looking for large categories, statistical classification techniques are efficient as soon as the training set is large enough. We introduce a two-step-exploration, classification-interactive strategy designed for category retrieval. The first step aims at getting a useful initial training set for the classification step. A stochastic image selection process is used instead of the usual strategy based on a similarity score ranking. This process is dedicated to explore the database in order to collect examples as various as possible of the searched category. The second step aims at providing the best classification between relevant and irrelevant images. Based on SVM, the classification applies an active learning strategy through user interaction. A quality assessment is carried out on the ANN and COREL databases in order to compare and validate our approach. (c) 2006 Elsevier B.V. All rights reserved.