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Skeleton-Based 3D Object Retrieval Using Retina-Like Feature Descriptor

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journal contribution
posted on 20.12.2019 by Xueqing Zhao, Xin Shi, Bo Yang, Quanli Gao, Zhaofei Yu, Jian K Liu, Yonghong Tian, Tiejun Huang
Skeleton-based 3D object retrieval is a very efficient method to query the sketch databases in numerous applications. However, few skeleton images are found so far in existing sketch benchmarks. In this paper, we provide an initial benchmark dataset consisting of skeleton sketches, including hand-drawn skeletons and skeletons extracted from 3D objects, and both of them are used to form a generic object class. Then we present a method for skeleton-based 3D object retrieval using a retina-like feature descriptor (S3DOR-RFD) based on the structural property of the human retina for processing complex visual information in a very efficient way. As part of the S3DOR-RFD algorithm, we combine artificial bee colony (ABC) in support vector machine (SVM) so as to improve the performance with automatic parameter selection, where one can make full use of the advantages of ABC and SVM to further improve the accuracy rate of 3D object retrieval. Experimental results indicate that skeleton sketches can be automatically distinguished from perspective sketches, and that the proposed S3DOR-RFD method works efficiently for selected object classes.

Funding

National Natural Science Foundation of China under Grant 61806160, Grant 61806011,Grant U1611461, and Grant 61806159

Shaanxi Association for Science and Technology of Colleges and Universities YouthTalent Development Program Grant 20190112

International Talent Exchange Program of Beijing Municipal Commission ofScience and Technology under Grant Z181100001018026

China Postdoctoral Science Foundation under Grant andGrant 2018M630036

National Postdoctoral Program for Innovative Talents under Grant BX20180005

Youth Innovation Team of Shaanxi Universities

History

Citation

IEEE Access, 7, 2019, pp. 157341 - 157352

Author affiliation

Department of Neuroscience, Psychology and Behaviour, College of Life Sciences

Version

VoR (Version of Record)

Published in

IEEE Access

Volume

7

Pagination

157341 - 157352

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

eissn

2169-3536

Acceptance date

17/09/2019

Copyright date

2019

Available date

27/09/2019

Publisher version

https://ieeexplore.ieee.org/abstract/document/8851210/authors#authors

Language

en

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