王胜法

个人信息Personal Information

副教授

博士生导师

硕士生导师

性别:男

毕业院校:大连理工大学

学位:博士

所在单位:软件学院、国际信息与软件学院

学科:软件工程. 计算机应用技术. 计算数学

办公地点:信息楼317

联系方式:0411-62274427 250066715@qq.com

电子邮箱:sfwang@dlut.edu.cn

扫描关注

论文成果

当前位置: 中文主页 >> 科学研究 >> 论文成果

NON-RIGID 3D SHAPE RETRIEVAL BASED ON MULTI-VIEW METRIC LEARNING

点击次数:

论文类型:会议论文

发表时间:2019-01-01

收录刊物:EI、CPCI-S

页面范围:441-446

关键字:Non-rigid shape retrieval; Multi-view learning; Metric learning; Marginal Fisher Analysis

摘要:This study presents a novel multi-view metric learning algorithm, which aims to improve 3D non-rigid shape retrieval. With the development of non-rigid 3D shape analysis, there exist many shape descriptors. The intrinsic descriptors can be explored to construct various intrinsic representations for non-rigid 3D shape retrieval task. The different intrinsic representations (features) focus on different geometric properties to describe the same 3D shape, which makes the representations are related. Therefore, it is possible and necessary to learn multiple metrics for different representations jointly. We propose an effective multi-view metric learning algorithm by extending the Marginal Fisher Analysis (MFA) into the multi-view domain, and exploring Hilbert-Schmidt Independence Criteria (HSCI) as a diversity term to jointly learning the new metrics. The different classes can be separated by MFA in our method. Meanwhile, HSCI is exploited to make the multiple representations to be consensus. The learned metrics can reduce the redundancy between the multiple representations, and improve the accuracy of the retrieval results. Experiments are performed on SHREC'10 benchmarks, and the results show that the proposed method outperforms the state-of-the-art non-rigid 3D shape retrieval methods.