A quasi-visibility graph based clique-extraction heuristic model for partitioning of planar shape

Sourav Saha, Ankita Mandal, Sayantan Rana and Priya Ranjan Sinha Mahapatra

International Journal of Applied Management Science, 2019, vol. 11, issue 1, 36-54

Abstract: This paper presents a graph theoretical model to partition polygonal approximation of a shape into visually meaningful constituent parts based on a heuristic approach. The proposed model introduces a new concept of approximated vertex-visibility graph termed as quasi-visibility graph to generate different viable cuts for partitioning the shape. In the shape representative graph, a maximal-clique perceptually corresponds to a distinguishable part. Based on this notion, we propose a heuristic based clique extraction strategy to decompose the shape exploring its quasi-visibility graph. A few refinement strategies are also attempted by exploring the options of: a) merging correlated parts for better visual interpretation; b) inserting antipodal points of reflex vertices in polygonal approximation for more possible viable cuts. The performance of the proposed model is evaluated by comparing partition-graphs of similar shapes. The partitioning based on the proposed model appears to be coherent with human observation and comparable with existing algorithms.

Keywords: shape partitioning; visibility graph; shape analysis; shape descriptor; quasi-visibility graph; clique-extraction heuristic; contour analysis; shape-graph. (search for similar items in EconPapers)
Date: 2019
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