A Geodesic Locality Canonical Correlation Analysis Method for Image Recognition

Huan XU; Shuzhi SU; Wenjing YAN; Yinghao DENG; Jun XIE

doi:10.11999/JEIT200123

Volume 42 Issue 11

Nov. 2020

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Article Navigation > Journal of Electronics & Information Technology > 2020 > 42(11): 2813-2818

Huan XU, Shuzhi SU, Wenjing YAN, Yinghao DENG, Jun XIE. A Geodesic Locality Canonical Correlation Analysis Method for Image Recognition[J]. Journal of Electronics & Information Technology, 2020, 42(11): 2813-2818. doi: 10.11999/JEIT200123

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Huan XU, Shuzhi SU, Wenjing YAN, Yinghao DENG, Jun XIE. A Geodesic Locality Canonical Correlation Analysis Method for Image Recognition[J]. Journal of Electronics & Information Technology, 2020, 42(11): 2813-2818. doi: 10.11999/JEIT200123

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A Geodesic Locality Canonical Correlation Analysis Method for Image Recognition

doi: 10.11999/JEIT200123

1.
College of Computer Science and Engineering, Anhui University of Science & Technology, Huainan 232001, China
2.
School of Computer and Information Engineering, Beijing Technology and Business University, Beijing 100037, China

Funds: The National Natural Science Foundation of China (61806006), The Anhui Province Natural Science Research Foundation of Institutions of Higher Learning (KJ2018A0083), The China Postdoctoral Science Foundation (2019M660149)

Received Date: 2020-02-21
Rev Recd Date: 2020-07-23

Available Online: 2020-07-23

Publish Date: 2020-11-16

Abstract

Abstract

Canonical Correlation Analysis (CCA) is a classic multi-modal feature learning method, which can learn low-dimensional features with the maximum correlation from different modalities. However, it is difficult for CCA to find the nonlinear manifold structures hidden in the sample spaces. This paper proposes a multi-modal feature learning method based on geodesic manifolds, namely Geodesic Locality Canonical Correlation Analysis (GeoLCCA).The geodesic distances are used to construct the geodesic scatters of low-dimensional correlation features, and the nonlinear correlation features with better discriminative power are learned by maximizing the between-modal correlation and minimizing the within-modal geodesic scatters. This paper not only analyzes the proposed method in theory, but also verifies the effective of the proposed method on the real-world image datasets.
- Image recognition,
- Canonical Correlation Analysis (CCA),
- Multi-modal feature learning,
- Manifold learning

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References(25)

References

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