Scale space clustering evolution for salient region detection on 3D deformable shapes. (November 2017)

• Record Type: Journal Article
• Authors: Wang, Xupeng; Sohel, Ferdous; Bennamoun, Mohammed; Guo, Yulan; Lei, Hang
• Is Part Of: Pattern recognition. Volume 71(2017:Nov.)
• Page Start: 414
• Page End: 427
• Publication Date: 2017-11
• Subjects: Deformable shape segmentation -- Salient region detection -- Diffusion geometry -- Clustering algorithm -- Persistent homology; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2017.05.018
• Languages: English
• ISSNs: 0031-3203

Abstract

Highlights: A salient region detection method on 3D deformable shapes is proposed. The method relies on clustering of a data set derived from the scale space of the auto diffusion function parameterized by t . There exists an evolution of the scalar field across time scales, and that all the salient regions on 3D shapes are exposed during this process. Abstract: Salient region detection without prior knowledge is a challenging task, especially for 3D deformable shapes. This paper presents a novel framework that relies on clustering of a data set derived from the scale space of the auto diffusion function. It consists of three major techniques: scalar field construction, shape segmentation initialization and salient region detection. We define the scalar field using the auto diffusion function at consecutive time scales to reveal shape features. Initial segmentation of a shape is obtained using persistence-based clustering, which is performed on the scalar field at a large time scale to capture the global shape structure. We propose two measures to assess the clustering both on a global and local level using persistent homology. From these measures, salient regions are detected during the evolution of the scalar field. Experimental results on three popular datasets demonstrate the superior performance of the proposed framework in region detection.