The Taub Faculty of Computer Science Events and Talks

The Bag-Of-Features (BOF) approaches are becoming central in large-scale image retrieval. The standard BOF method is orderless, completely omitting geometric configuration of visual words. The geometrical information is usually involved only in the post-processing spatial verification step usually implemented with the RANdom SAmple Consensus (RANSAC) algorithm. To enable visual search in real-time, RANSAC can be applied only to a relatively small number of top candidates due to its computational requirements. In this work, we propose an alternative method to perform accurate spatial verification with a significantly lower computational cost. Experimental results show that the proposed method outperforms the baseline BOF, and achieves similar performance as RANSAC based spatial verification, despite the major difference in complexity.