Large scale image retrieval with visual groups
Bag-of-visual words (BoW) representation has been widely used in the large scale image retrieval. Though efficient, it ignores the geometric correlation among visual words, whereas the geometric verification has demonstrated its effectiveness in image retrieval. In this paper, we propose a new representation - visual group, to improve the retrieval precision by grouping the geometrically related features based on the inclusion relationship between features at different scales. A visual group consists of a master feature and several member features covered by the master feature. The geometric constraint inside each group is introduced into visual group matching for efficient geometric verification. Experimental evaluation on the dataset Oxford5K+Flickr1M shows that our visual group based image search approach outperforms BoW and the state-of-the-art visual phrase based schemes.