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Image-based 3D model retrieval using manifold learning

  • Pan-pan MuEmail author
  • San-yuan Zhang
  • Yin Zhang
  • Xiu-zi Ye
  • Xiang Pan
Article

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.

Key words

Model retrieval Euclidean space Riemannian manifold Hilbert space Metric learning 

CLC number

TP391 

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Copyright information

© Editorial Office of Journal of Zhejiang University Science and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.College of Computer Science and TechnologyZhejiang UniversityHangzhouChina
  2. 2.College of Mathematics and Information ScienceWenzhou UniversityWenzhouChina
  3. 3.College of Computer Science and TechnologyZhejiang University of TechnologyHangzhouChina

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