Scale space clustering evolution for salient region detection on 3D deformable shapes

PATTERN RECOGNITION

卷:71

Article

Wang, Xupeng^1,2  Sohel, Ferdous³  Bennamoun, Mohammed²  Guo, Yulan^2,4  Lei, Hang¹ 

[1] Univ Elect Sci & Technol China, Sch Informat & Software Engn, Chengdu, Sichuan, Peoples R China

[2] Univ Western Australia, Sch Comp Sci & Software Engn, Perth, WA, Australia

[3] Murdoch Univ, Sch Engn & Informat Technol, Perth, WA, Australia

[4] Natl Univ Def Technol, Coll Elect Sci & Engn, Changsha, Hunan, Peoples R China

关键词: Deformable shape segmentation;    Salient region detection;    Diffusion geometry;    Clustering algorithm;    Persistent homology;   

DOI  :  10.1016/j.patcog.2017.05.018

来源: Elsevier

【 摘 要 】

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. (C) 2017 Published by Elsevier Ltd.

【 授权许可】

Free