Name
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. (C) The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1117/1.JEI.23.4.043007 | JOURNAL OF ELECTRONIC IMAGING |
Keywords | Field | DocType |
intrasimilarity,interdiscrimination,clustering-weighted,SIFT,sparse representation-based classification | Scale-invariant feature transform,Computer vision,Pattern recognition,Computer science,Sparse approximation,Artificial intelligence,Associative array,Cluster analysis | Journal |
Volume | Issue | ISSN |
23 | 4 | 1017-9909 |
Citations | PageRank | References |
2 | 0.37 | 22 |
Authors | ||
3 | ||