Clustering-weighted SIFT-based classification method via sparse representation

Bo Sun; Feng Xu; Jun He

doi:10.1117/1.JEI.23.4.043007

14 July 2014 Clustering-weighted SIFT-based classification method via sparse representation

Bo Sun, Feng Xu, Jun He

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Journal of Electronic Imaging, Vol. 23, Issue 4, 043007 (July 2014). https://doi.org/10.1117/1.JEI.23.4.043007

Abstract

In recent years, sparse representation-based classification (SRC) has received significant attention due to its high recognition rate. However, the original SRC method requires a rigid alignment, which is crucial for its application. Therefore, features such as SIFT descriptors are introduced into the SRC method, resulting in an alignment-free method. However, a feature-based dictionary always contains considerable useful information for recognition. We explore the relationship of the similarity of the SIFT descriptors to multitask recognition and propose a clustering-weighted SIFT-based SRC method (CWS-SRC). The proposed approach is considerably more suitable for multitask recognition with sufficient samples. Using two public face databases (AR and Yale face) and a self-built car-model database, the performance of the proposed method is evaluated and compared to that of the SRC, SIFT matching, and MKD-SRC methods. Experimental results indicate that the proposed method exhibits better performance in the alignment-free scenario with sufficient samples.

CC BY: © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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Bo Sun, Feng Xu, and Jun He "Clustering-weighted SIFT-based classification method via sparse representation," Journal of Electronic Imaging 23(4), 043007 (14 July 2014). https://doi.org/10.1117/1.JEI.23.4.043007

Published: 14 July 2014

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