Encoding Spatial Context for Large-Scale Partial-Duplicate Web Image Retrieval  

作　　者：周文罡 李厚强 卢亦娟 田奇 

机构地区：[1]Chinese Academy of Sciences Key Laboratory of Technology in Geo-Spatial Information Processing and Application System University of Science and Technology of China [2]Department of Electronic Engineering and Information Science, University of Science and Technology of China [3]Department of Computer Science, Texas State University,San Marcos, TX 78666, U.S.A. Member,ACM,IEEE [4]Department of Computer Science, University of Texas at San Antonio,San Antonio, TX 78249, U.S.A. Senior Member,IEEE

出　　处：《Journal of Computer Science & Technology》2014年第5期837-848,共12页计算机科学技术学报（英文版）

基　　金：supported in part to Dr.Wen-Gang Zhou by the Fundamental Research Funds for the Central Universities of China under Grant Nos.WK2100060014 and WK2100060011;the Start-Up Funding from the University of Science and Technology of China under Grant No.KY2100000036;the Open Project of Beijing Multimedia and Intelligent Software Key Laboratory in Beijing University of Technology,and the sponsor from Intel ICRI MNC project;in part to Dr.Hou-Qiang Li by the National Natural Science Foundation of China(NSFC)under Grant Nos.61325009,61390514,and 61272316;in part to Dr.Yijuan Lu by the Army Research Office(ARO)of USA under Grant No.W911NF-12-1-0057;the National Science Foundation of USA under Grant No.CRI 1305302;in part to Dr.Qi Tian by ARO under Grant No.W911NF-12-1-0057;the Faculty Research Award by NEC Laboratories of America,respectively;was supported in part by NSFC under Grant No.61128007

摘　　要：Many recent state-of-the-art image retrieval approaches are based on Bag-of-Visual-Words model and represent an image with a set of visual words by quantizing local SIFT（scale invariant feature transform） features. Feature quantization reduces the discriminative power of local features and unavoidably causes many false local matches between images, which degrades the retrieval accuracy. To filter those false matches, geometric context among visual words has been popularly explored for the verification of geometric consistency. However, existing studies with global or local geometric verification are either computationally expensive or achieve limited accuracy. To address this issue, in this paper, we focus on partialduplicate Web image retrieval, and propose a scheme to encode the spatial context for visual matching verification. An efficient affine enhancement scheme is proposed to refine the verification results. Experiments on partial-duplicate Web image search, using a database of one million images, demonstrate the effectiveness and efficiency of the proposed approach.Evaluation on a 10-million image database further reveals the scalability of our approach.Many recent state-of-the-art image retrieval approaches are based on Bag-of-Visual-Words model and represent an image with a set of visual words by quantizing local SIFT（scale invariant feature transform） features. Feature quantization reduces the discriminative power of local features and unavoidably causes many false local matches between images, which degrades the retrieval accuracy. To filter those false matches, geometric context among visual words has been popularly explored for the verification of geometric consistency. However, existing studies with global or local geometric verification are either computationally expensive or achieve limited accuracy. To address this issue, in this paper, we focus on partialduplicate Web image retrieval, and propose a scheme to encode the spatial context for visual matching verification. An efficient affine enhancement scheme is proposed to refine the verification results. Experiments on partial-duplicate Web image search, using a database of one million images, demonstrate the effectiveness and efficiency of the proposed approach.Evaluation on a 10-million image database further reveals the scalability of our approach.

关 键 词：large-scale image retrieval spatial context coding spatial verification affine estimation 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术] TU984.12[自动化与计算机技术—计算机科学与技术]