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Shape recognition from large image libraries by inexact graph matching
Affiliation:1. College of Sciences, National University of Defense Technology, Changsha 410073, Hunan, China;2. Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing, College of Information Science and Engineering, Hunan Normal University, Changsha 410081, Hunan, China;3. State Key Lab on Integrated Services Networks, School of Cyber Engineering, Xidian University, Xi’an 710071, Shanxi, China;4. School of Computer Science, Guangzhou University, Guangzhou 510006, Guangdong, China;1. Department of Civil Engineering, Shanghai Jiao Tong University, Shanghai, China;2. Shanghai Key Laboratory for Digital Maintenance of Buildings and Infrastructure, Shanghai, China;1. Dept. of Cartography, GIS & Remote Sensing; Institute of Geography; Georg-August University of Göttingen, Göttingen, Germany;2. Dept. of Surveying Engineering, Wollega University, Nekemte, Ethiopia;3. Dept. of Civil (Geomatic) Engineering, Indian Institute of Technology, Roorkee, India
Abstract:This paper describes a graph-matching technique for recognising line-pattern shapes in large image databases. The methodological contribution of the paper is to develop a Bayesian matching algorithm that uses edge-consistency and node attribute similarity. This information is used to determine the a posteriori probability of a query graph for each of the candidate matches in the database. The node feature-vectors are constructed by computing normalised histograms of pairwise geometric attributes. Attribute similarity is assessed by computing the Bhattacharyya distance between the histograms. Recognition is realised by selecting the candidate from the database which has the largest a posteriori probability.
Keywords:
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