Image Retrieval with Geometry-Preserving Visual Phrases

The most popular approach to large scale image retrieval is based on the bag-of-visual-word (BoV) representation of images. The spatial information is usually reintroduced as a post-processing step to re-rank the retrieved images, through a spatial verification like RANSAC. Since the spatial verification techniques are computationally expensive, they can be applied only to the top images in the initial ranking. In this paper, we propose an approach that can encode more spatial information into BoV representation and that is efficient enough to be applied to large-scale databases. Other works pursuing the same purpose have proposed exploring the word co-occurrences in the neighborhood areas. Our approach encodes more spatial information through the geometry-preserving visual phrases (GVP). In addition to co-occurrences, the GVP method also captures the local and long-range spatial layouts of the words. Our GVP based searching algorithm increases little memory usage or computational ...

• Type: Journal
• Year: 2011
• Where: CVPR
• Authors: Yimeng Zhang, Zhaoyin Jia, Tsuhan Chen
• Keywords: Computer Vision, CVPR 2011, Images, Spatial, Spatial Information