SIFT match verification by geometric coding for large-scale partial-duplicate web image search
Abstract
Most large-scale image retrieval systems are based on the bag-of-visual-words model. However, the traditional bag-of-visual-words model does not capture the geometric context among local features in images well, 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 methods on global geometric verification are either computationally expensive to ensure real-time response, or cannot handle rotation well. To solve the preceding problems, in this article, we propose a novel geometric coding algorithm, to encode the spatial context among local features for large-scale partial-duplicate Web image retrieval. Our geometric coding consists of geometric square coding and geometric fan coding, which describe the spatial relationships of SIFT features into three geo-maps for global verification to remove geometrically inconsistent SIFT matches. Our approach is not only computationally efficient, but also effective in detecting partial-duplicate images with rotation, scale changes, partial-occlusion, and background clutter.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Feb 01, 2013
- Source ID
- 10.1145/2422956.2422960
Entities
People
- Houqiang Li
- Qi Tian
- Wengang Zhou
- Yijuan Lu
Organizations
- Army Research Office
- Division of Information and Intelligent Systems
- NEC Corporation of America
- Texas State University
- United States Department of Defense
- University of Science and Technology of China
- University of Texas at San Antonio