Object matching with hierarchical skeletons. (July 2016)

• Record Type: Journal Article
• Authors: Yang, Cong; Tiebe, Oliver; Shirahama, Kimiaki; Grzegorzek, Marcin
• Is Part Of: Pattern recognition. Volume 55(2016:Jul.)
• Page Start: 183
• Page End: 197
• Publication Date: 2016-07
• Subjects: Skeletonisation -- Hierarchical skeleton -- Skeleton evolution -- Object representation -- Object matching; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2016.01.022
• Languages: English
• ISSNs: 0031-3203

Abstract

Abstract: The skeleton of an object provides an intuitive and effective abstraction which facilitates object matching and recognition. However, without any human interaction, traditional skeleton-based descriptors and matching algorithms are not stable for deformable objects. Specifically, some fine-grained topological and geometrical features would be discarded if the skeleton was incomplete or only represented significant visual parts of an object. Moreover, the performance of skeleton-based matching highly depends on the quality and completeness of skeletons. In this paper, we propose a novel object representation and matching algorithm based on hierarchical skeletons which capture the shape topology and geometry through multiple levels of skeletons. For object representation, we reuse the pruned skeleton branches to represent the coarse- and fine-grained shape topological and geometrical features. Moreover, this can improve the stability of skeleton pruning without human interaction. We also propose an object matching method which considers both global shape properties and fine-grained deformations by defining singleton and pairwise potentials for similarity computation between hierarchical skeletons. Our experiments attest our hierarchical skeleton-based method a significantly better performance than most existing shape-based object matching methods on six datasets, achieving a 99.21% bulls-eye score on the MPEG7 shape dataset. Abstract : Highlights: It represents the coarse- and fine-grained shape topological and geometrical features. It improves the stability of skeleton pruning without human interaction. It considers both global and fine shape properties by different potential functions. It achieves a better performance than most existing methods on six datasets. Experiments attest our method a better performance than most related approaches. We achieve a 99.21% bulls-eye score on the MPEG7 shape dataset.