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

作     者：Wang, Xupeng Sohel, Ferdous Bennamoun, Mohammed Guo, Yulan Lei, Hang 

作者机构：Univ Elect Sci & Technol China Sch Informat & Software Engn Chengdu Sichuan Peoples R China Univ Western Australia Sch Comp Sci & Software Engn Perth WA Australia Murdoch Univ Sch Engn & Informat Technol Perth WA Australia Natl Univ Def Technol Coll Elect Sci & Engn Changsha Hunan Peoples R China 

出 版 物：《PATTERN RECOGNITION》 (图形识别)

年 卷 期：2017年第71卷

页      面：414-427页

核心收录：

学科分类：0808[工学-电气工程] 08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：China Scholarship Council National Natural Science Foundation of China [61602499, 61471371] National Postdoctoral Program for Innovative Talents [BX201600172] Australian Research Council [DE120102960, DP150100294, DP150104251] 

主　　题：Deformable shape segmentation Salient region detection Diffusion geometry Clustering algorithm Persistent homology 

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.