Fast Object Retrieval Using Direct Spatial Matching

The conventional bag-of-visual-words (BoW) model is popular for the large-scale object retrieval system but suffers from the critical drawback of ignoring spatial information . RANSAC-based methods attempt to remedy this drawback, but often require traversing all the feature matches for each hypothesis , leading to the heavy computational cost which limits the number of gallery images to be verified for each online query. We propose an efficient direct spatial matching (DSM) approach to directly estimate the scale variation using region sizes, in which all feature matches voted for estimating geometric transformation . DSM is much faster than RANSAC-based methods and exhaustive enumeration approaches. A logarithmic term frequency- inverse document frequency (log tf-idf) weighting scheme is introduced to boost the performance of the base system. We have conducted extensive experimental evaluations on four benchmark datasets for object retrieval. The proposed DSM method, together with a carefully-tailored reranking scheme, achieves the state-of-the-art results on the Oxford buildings and Paris datasets, which demonstrates the efficacy and scalability of our novel DSM technique for large scale object retrieval systems.