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CLC number: TP391

On-line Access: 2018-12-14

Received: 2016-12-01

Revision Accepted: 2017-05-22

Crosschecked: 2018-11-27

Cited: 0

Clicked: 1720

Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Pan-pan Mu

https://orcid.org/0000-0001-9224-662X

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Frontiers of Information Technology & Electronic Engineering  2018 Vol.19 No.11 P.1397-1408

http://doi.org/10.1631/FITEE.1601764


Image-based 3D model retrieval using manifold learning


Author(s):  Pan-pan Mu, San-yuan Zhang, Yin Zhang, Xiu-zi Ye, Xiang Pan

Affiliation(s):  College of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China; more

Corresponding email(s):   mupanpan0927@zju.edu.cn

Key Words:  Model retrieval, Euclidean space, Riemannian manifold


Pan-pan Mu, San-yuan Zhang, Yin Zhang, Xiu-zi Ye, Xiang Pan. Image-based 3D model retrieval using manifold learning[J]. Frontiers of Information Technology & Electronic Engineering, 2018, 19(11): 1397-1408.

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author="Pan-pan Mu, San-yuan Zhang, Yin Zhang, Xiu-zi Ye, Xiang Pan",
journal="Frontiers of Information Technology & Electronic Engineering",
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publisher="Zhejiang University Press & Springer",
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Abstract: 
We propose a new framework for image-based three-dimensional (3D) model retrieval. We first model the query image as a Euclidean point. Then we model all projected views of a 3D model as a symmetric positive definite (SPD) matrix, which is a point on a riemannian manifold. Thus, the image-based 3D model retrieval is reduced to a problem of Euclid-to-Riemann metric learning. To solve this heterogeneous matching problem, we map the euclidean space and SPD riemannian manifold to the same high-dimensional hilbert space, thus shrinking the great gap between them. Finally, we design an optimization algorithm to learn a metric in this hilbert space using a kernel trick. Any new image descriptors, such as the features from deep learning, can be easily embedded in our framework. Experimental results show the advantages of our approach over the state-of-the-art methods for image-based 3D model retrieval.

利用流形学习进行基于图像的三维模型检索

摘要:提出一种基于图像的三维模型检索新框架。把查询的图像建模为欧氏空间的一个点,把三维模型所有投影视图建模为一个对称正定矩阵,并视该对称正定矩阵为黎曼流形上的一个点。于是,基于图像的三维模型检索简化为欧氏空间到黎曼流形上的度量学习。为解决异质匹配问题,把欧式空间和对称正定矩阵黎曼空间映射到同一个高维希尔伯特空间,极大缩小彼此之间语义鸿沟。最后,使用核方法设计一个优化算法学习映射。任何新图像描述符,比如深度学习特征描述符,可以很容易嵌入该框架。实验结果表明,该方法相较目前最新基于图像的三维模型检索方法有一定优势。

关键词:模型检索;欧式空间;黎曼流形;希尔伯特空间;度量学习

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

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