Sketch-based 3D Model Retrieval via Multi-feature Fusion

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成果类型：

期刊论文、会议论文

作者：

Wen, Yafei*;Zou, Changqing;Liu, Jianzhuang;Du, Shuze;Chen, Shifeng

通讯作者：

Wen, Yafei

作者机构：

[Chen, Shifeng; Wen, Yafei; Zou, Changqing; Liu, Jianzhuang] Chinese Acad Sci, Shenzhen Inst Adv Technol, Shenzhen Key Lab Comp Vis & Pat Rec, Shenzhen, Peoples R China.

[Du, Shuze] Chinese Acad Sci, Chengdu Inst Comp Applicat, Chengdu, Peoples R China.

[Du, Shuze; Wen, Yafei; Zou, Changqing] Univ Chinese Acad Sci, Beijing, Peoples R China.

[Zou, Changqing] Hengyang Normal Univ, Dept Phys & Elect Informat Sci, Hengyang, Peoples R China.

[Chen, Shifeng; Wen, Yafei; Zou, Changqing; Liu, Jianzhuang] Chinese Univ Hong Kong, Hong Kong, Hong Kong, Peoples R China.

通讯机构：

[Wen, Yafei] C

Chinese Acad Sci, Shenzhen Inst Adv Technol, Shenzhen Key Lab Comp Vis & Pat Rec, Shenzhen, Peoples R China.

语种：

英文

关键词：

3D model retrieval;bag-of-features;feature fusion;shape descriptor

期刊：

Proceedings - International Conference on Pattern Recognition

ISSN：

1051-4651

年：

2014

页码：

4570-4575

会议名称：

22nd International Conference on Pattern Recognition (ICPR)

会议论文集名称：

International Conference on Pattern Recognition

会议时间：

AUG 24-28, 2014

会议地点：

Swedish Soc Automated Image Anal, Stockholm, SWEDEN

会议主办单位：

Swedish Soc Automated Image Anal

会议赞助商：

IEEE Comp Soc, IAPR, Linkopings Univ, Lunds Univ, Uppsala Univ, e Sci Collaborat, Swedish Soc Automated Image Anal, Stockhoms Stad, Swedish e Sci Res Ctr, SICK, Autoliv, IBM Res, Int Journal Automat & Comp

出版地：

10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA

出版者：

IEEE COMPUTER SOC

ISBN：

978-1-4799-5208-3

DOI：

10.1109/ICPR.2014.782

机构署名：

本校为其他机构

院系归属：

物理与电子工程学院

摘要：

Sketch-based 3D model retrieval provides a convenient way for users to search for 3D models by sketches. Traditionally, this task is converted to a sketch-based 2D shape retrieval problem by projecting 3D models to 2D images. Local invariant features have been widely used to tackle this problem. However, it suffers from the lack of global context and easily fails when images of different 3D models share multiple similar regions. In this paper, we propose a joint description by fusing local statistical structures and global spatial features. Our description is invariant to scale, translate and ...

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Sketch-based 3D Model Retrieval via Multi-feature Fusion

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