In this paper, a novel multi-color feature selection method is proposed for person re-identification. Firstly, multi-color features, which consisting of HSV, LAB, RGB and nRnG color features, were extracted and concatenated into a whole feature vector. Then the D-optimal Partial Least Squares feature selection method was adopted to select an optimal feature subset that could minimize the variance of the regression model. Finally, an asymmetric distance model for similarity matching was utilized to observe distinctive features from a different perspective. Experimental results show that rank 1 performance of the proposed method were 48.67%, 63.12% and 65.04% respectively on the VIPeR, Prid_450s and CUHK01 databases, which have achieved state-of-art performances.

A Novel Multi-Color Feature Selection Method for Person Re-identification / Y. Zhai, L. Chen, L. Cao, W. Deng, Y. Zhi, J. Gan, Y. Xu, J. Zeng, V. Piuri, F. Scotti - In: 2018 14th IEEE International Conference on Signal Processing (ICSP)[s.l] : IEEE, 2018. - ISBN 9781538646731. - pp. 1087-1092 (( Intervento presentato al 14. convegno International Conference on Signal Processing (ICSP) tenutosi a Beijing nel 2018.

A Novel Multi-Color Feature Selection Method for Person Re-identification

V. Piuri;F. Scotti
2018

Abstract

In this paper, a novel multi-color feature selection method is proposed for person re-identification. Firstly, multi-color features, which consisting of HSV, LAB, RGB and nRnG color features, were extracted and concatenated into a whole feature vector. Then the D-optimal Partial Least Squares feature selection method was adopted to select an optimal feature subset that could minimize the variance of the regression model. Finally, an asymmetric distance model for similarity matching was utilized to observe distinctive features from a different perspective. Experimental results show that rank 1 performance of the proposed method were 48.67%, 63.12% and 65.04% respectively on the VIPeR, Prid_450s and CUHK01 databases, which have achieved state-of-art performances.
multi-color features; feature selection; D-optimal partial least squares; person re-identification
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/2434/634219
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