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Active Boosting for Interactive Object Retrieval

This paper presents a new algorithm based on boosting for interactive object retrieval in images. Recent works propose ”online boosting” algorithms where weak classifier sets are iteratively trained from data. These algorithms are proposed for visual tracking in videos, and are not well adapted to ”online boosting” for interactive retrieval. We propose in this paper to iteratively build weak classifiers from images, labeled as positive by the user during a retrieval session. A novel active learning strategy for the selection of images for user annotation is also proposed. This strategy is used to enhance the strong classifier resulting from ”boosting” process, but also to build new weak classifiers. Experiments have been carried out on a generalist database in order to compare the proposed method to a SVM based reference approach.