Kernelized cross-view quadratic discriminant analysis for person re-identification

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In person re-identification, Keep It Simple and Straightforward MEtric (KISSME) is known as a practical distance metric learning method. Typically, kernelization improves the performance of metric learning methods. Nevertheless, deriving KISSME on a reproducing kernel Hilbert space is a non-trivial problem. Nyström method approximates the Hilbert space in low-dimensional Euclidean space, and the application of KISSME is straightforward, yet it fails to preserve discriminative information. To utilize KISSME in a discriminative subspace of the Hilbert space, we propose a kernel extension of Cross-view Discriminant Analysis (XQDA) which learns a discriminative low-dimensional subspace, and simultaneously KISSME in the learned subspace. We show with the standard kernel trick, the kernelized XQDA results in the case when the empirical kernel vector is used as the input of XQDA. Experimental results on benchmark datasets show the kernelized XQDA outperforms XQDA and Nyström-KISSME.

Original languageEnglish
Title of host publicationProceedings of the 16th International Conference on Machine Vision Applications, MVA 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9784901122184
DOIs
Publication statusPublished - May 1 2019
Event16th International Conference on Machine Vision Applications, MVA 2019 - Tokyo, Japan
Duration: May 27 2019May 31 2019

Publication series

NameProceedings of the 16th International Conference on Machine Vision Applications, MVA 2019

Conference

Conference16th International Conference on Machine Vision Applications, MVA 2019
CountryJapan
CityTokyo
Period5/27/195/31/19

Fingerprint

Hilbert spaces
Discriminant analysis

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Signal Processing
  • Computer Vision and Pattern Recognition

Cite this

Matsukawa, T., & Suzuki, E. (2019). Kernelized cross-view quadratic discriminant analysis for person re-identification. In Proceedings of the 16th International Conference on Machine Vision Applications, MVA 2019 [8757990] (Proceedings of the 16th International Conference on Machine Vision Applications, MVA 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.23919/MVA.2019.8757990

Kernelized cross-view quadratic discriminant analysis for person re-identification. / Matsukawa, Tetsu; Suzuki, Einoshin.

Proceedings of the 16th International Conference on Machine Vision Applications, MVA 2019. Institute of Electrical and Electronics Engineers Inc., 2019. 8757990 (Proceedings of the 16th International Conference on Machine Vision Applications, MVA 2019).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Matsukawa, T & Suzuki, E 2019, Kernelized cross-view quadratic discriminant analysis for person re-identification. in Proceedings of the 16th International Conference on Machine Vision Applications, MVA 2019., 8757990, Proceedings of the 16th International Conference on Machine Vision Applications, MVA 2019, Institute of Electrical and Electronics Engineers Inc., 16th International Conference on Machine Vision Applications, MVA 2019, Tokyo, Japan, 5/27/19. https://doi.org/10.23919/MVA.2019.8757990
Matsukawa T, Suzuki E. Kernelized cross-view quadratic discriminant analysis for person re-identification. In Proceedings of the 16th International Conference on Machine Vision Applications, MVA 2019. Institute of Electrical and Electronics Engineers Inc. 2019. 8757990. (Proceedings of the 16th International Conference on Machine Vision Applications, MVA 2019). https://doi.org/10.23919/MVA.2019.8757990
Matsukawa, Tetsu ; Suzuki, Einoshin. / Kernelized cross-view quadratic discriminant analysis for person re-identification. Proceedings of the 16th International Conference on Machine Vision Applications, MVA 2019. Institute of Electrical and Electronics Engineers Inc., 2019. (Proceedings of the 16th International Conference on Machine Vision Applications, MVA 2019).
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