Precision-Oriented Active Selection for Interactive Image Retrieval

Active learning methods have been considered with an increased interest in the content-based image retrieval (CBIR) community. Those methods used to be based on classical classification problems, and do not deal with the particular characteristics of the CBIR. One of those characteristics is the criteria to optimize, for instance the error of generalization for classification, which is not the most adapted to CBIR context. Thus, we introduce in this paper an active selection which chooses the image the user should label such as the Mean Average Precision is increased. The method is smartly combined with previous propositions, and lead to a fast and efficient active learning scheme. Experiments on a large database have carried out in order to compare our approach to several other methods.

• Type: Conference
• Year: 2006
• Where: ICIP
• Authors: Philippe Henri Gosselin, Matthieu Cord
• Keywords: Active Learning Methods, Active Learning Scheme, Content-based Image Retrieval, ICIP 2006, Image Processing, Mean Average Precision