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CLC number: TP391
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 2018-11-27
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Image-based 3D model retrieval using manifold learning
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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.
@article{title="Image-based 3D model retrieval using manifold learning",
author="Pan-pan Mu, San-yuan Zhang, Yin Zhang, Xiu-zi Ye, Xiang Pan",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="19",
number="11",
pages="1397-1408",
year="2018",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.1601764"
}
%0 Journal Article
%T Image-based 3D model retrieval using manifold learning
%A Pan-pan Mu
%A San-yuan Zhang
%A Yin Zhang
%A Xiu-zi Ye
%A Xiang Pan
%J Frontiers of Information Technology & Electronic Engineering
%V 19
%N 11
%P 1397-1408
%@ 2095-9184
%D 2018
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.1601764
TY - JOUR
T1 - Image-based 3D model retrieval using manifold learning
A1 - Pan-pan Mu
A1 - San-yuan Zhang
A1 - Yin Zhang
A1 - Xiu-zi Ye
A1 - Xiang Pan
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 19
IS - 11
SP - 1397
EP - 1408
%@ 2095-9184
Y1 - 2018
PB - Zhejiang University Press & Springer
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DOI - 10.1631/FITEE.1601764
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.
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