A distance measure between labeled combinatorial maps

  • Authors:

  • Tao Wang;Guojun Dai;Bingbing Ni;De Xu;Francois Siewe

  • Affiliations:

  • School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044, China;Computer School, Hangzhou Dianzi University, Hangzhou 310018, China;Advanced Digital Sciences Center, Singapore 138632, Singapore;School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044, China;Software Technology Research Laboratory, Faculty of Technology, De Montfort University, Leicester LE1 9BH, Leics England, United Kingdom

  • Venue:
  • Computer Vision and Image Understanding
  • Year:
  • 2012

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Abstract

Combinatorial maps are widely used in image representation and processing, however map matching problems have not been extensively researched. This paper addresses the problem of inexact matching between labeled combinatorial maps. First, the concept of edit distance is extended to combinatorial maps, and then used to define mapping between combinatorial maps as a sequence of edit operations that transforms one map into another. Subsequently, an optimal approach based on A^* algorithm and an approximate approach based on Greedy algorithm are proposed to compute the distance between combinatorial maps. Experimental results show that the proposed inexact map matching approach produces richer search results than the exact map matching technique by tolerating small difference between maps. The proposed approach performs better in practice than the previous approach based on maximum common submap which cannot be directly used for comparing labels on the maps.