Generalized maps are widely used to model the topology of nD objects (such as images) by means of incidence and adjacency relationships between cells (vertices, edges, faces, volumes, ⋯). In this paper, we define a first error-tolerant distance measure for comparing generalized maps, which is an important issue for image processing and analysis. This distance measure is defined by means of the size of a largest common submap, in a similar way as a graph distance measure may be defined by means of the size of a largest common subgraph. We show that this distance measure is a metric, and we introduce a greedy randomized algorithm which allows us to efficiently compute an upper bound of it. © 2011 Springer-Verlag Berlin Heidelberg.
CITATION STYLE
Combier, C., Damiand, G., & Solnon, C. (2011). Measuring the distance of generalized Maps. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 6658 LNCS, 82–91. https://doi.org/10.1007/978-3-642-20844-7_9
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