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Journal of Electronic Imaging • Open Access

Clustering-weighted SIFT-based classification method via sparse representation
Author(s): Bo Sun; Feng Xu; Jun He

Paper 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.

Paper Details

Date Published: 14 July 2014
PDF: 7 pages
J. Electron. Imaging. 23(4) 043007 doi: 10.1117/1.JEI.23.4.043007
Published in: Journal of Electronic Imaging Volume 23, Issue 4
Show Author Affiliations
Bo Sun, Beijing Normal Univ. (China)
Feng Xu, Beijing Normal Univ. (China)
Jun He, Beijing Normal Univ. (China)


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