The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.
The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.
Patent No.:
Date of Patent:
Aug. 09, 2016
Filed:
Nov. 09, 2012
Board of Regents of the University of Texas System, Austin, TX (US);
Qi Tian, Helotes, TX (US);
Wengang Zhou, Hefei, CN;
Houqiang Li, Hefei, CN;
Yijuan Lu, San Marcos, TX (US);
Board of Regents of the University of Texas System, Austin, TX (US);
Texas State University, San Marcos, TX (US);
University of Science and Technology of China, Anhui, CN;
Abstract
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.