In:
International Journal of Image and Graphics, World Scientific Pub Co Pte Ltd, Vol. 03, No. 03 ( 2003-07), p. 503-521
Abstract:
Feature extraction is very important in the subject of pattern recognition. Sparse coding is an approach for extracting the independent features of an image. The image features extracted by sparse coding have led to better recognition performance as compared to those from traditional PCA-based methods. A new discriminatory sparse coding (DSC) algorithm is proposed in this paper to further improve the classification performance. Based on reinforcement learning, DSC encodes the training samples by individual class rather than by individual image as in sparse coding. Having done that it will produce a set of features with large and small intraclass variations, which is very suitable for recognition tasks. Experiments are performed on face image feature extraction and recognition. Compared with the traditional PCA- and ICA-based methods, DSC shows a much better recognition performance.
Type of Medium:
Online Resource
ISSN:
0219-4678
,
1793-6756
DOI:
10.1142/S0219467803001135
Language:
English
Publisher:
World Scientific Pub Co Pte Ltd
Publication Date:
2003
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