Geometric coding for billion-scale partial-duplicate image search

摘要

Most of large-scale image retrieval systems are based on Bag-of-Visual-Words model. However, traditional Bag-of-Visual-Words model does not well capture the geometric context among local features in images, which plays an important role in image retrieval. In order to fully explore geometric context of all visual words in images, efficient global geometric verification methods have been attracting lots of attention. Unfortunately, current existing global geometric verification methods are either computationally expensive to ensure real-time response. To solve the above problems, a novel geometric coding algorithm is used to encode the spatial context among local features for large scale partial duplicate image retrieval. With geometric square coding and geometric fan coding, our geometric coding scheme encodes the spatial relationships of local features into three geo-maps, which are used for global verification to remove spatially inconsistent matches. This approach is not only computationally efficient, but also effective in detecting duplicate images with rotation, scale changes, occlusion, and background clutter.