Active learning techniques for user interactive systems: application to image retrieval

This paper propose a new active learning method based on RankBoost for interactive search.. The pro- posed method build a set of weak classifiers and use an active method to select

At the end of such a retrieval session, the semantic kernel is updated using the semantic learning method with the 50 labels.. Every 1,000 retrieval sessions, the semantic kernel K t

We propose an active learning method to select the most difficult images to classify, and to reduce the re- dundancy in the training data set.. Irrevelant unlabelled data

With 25 positive labels (the usual case at the end of retrieval sessions), base kernel pools with 64 descriptors per image have 1600 elements, and base kernel pools with 21

We experimentally noticed that selecting images close to the boundary between relevant and irrelevant classes is definitively efficient, but when considering a subset of images close

This strategy, based on past research works in text retrieval, is suitable for the context of batch learning, when a large training set can be built either by using a strong

When the user is looking for large categories, statistical classification techniques are efficient as soon as the training set is large enough.. The first step aims at getting a

Target search strategy is working as follows: the user presents an example of the images he is looking for, and the system extracts from the database the images most similar to