Interactive objects retrieval with efficient boosting

This paper presents an efficient local features boosting strategy for interactive objects retrieval tasks such as on-line supervised learning or relevance feedback. The prediction time complexity of most existing methods is indeed usually linear in dataset size since the retrieval works by applying a trained classifier on the images of the dataset one by one. In our method, the trained classifier can be computed directly on the whole dataset in sublinear time thanks to distance-based weak classifiers. The idea is to speed-up drastically the prediction of each weak classifier on the whole dataset by performing approximate range queries with an efficient similarity search structure. Experiments on Caltech 256 dataset show that the technique is up to 250 times faster than the naive exhaustive method. Thanks to this efficiency improvement, we developed a relevance feedback mechanism on image regions freely selected by the user and we show how it improves the effectiveness of the retrieval.