Article ID Journal Published Year Pages File Type
530767 Pattern Recognition 2014 10 Pages PDF
Abstract

•A synergistic combination of K-SVD and SRC Note.•No extra parameter is introduced.•More accurate recognition rate.•Fewer inputs/assumptions.

The paper presents a supervised discriminative dictionary learning algorithm specially designed for classifying HEp-2 cell patterns. The proposed algorithm is an extension of the popular K-SVD algorithm: at the training phase, it takes into account the discriminative power of the dictionary atoms and reduces their intra-class reconstruction error during each update. Meanwhile, their inter-class reconstruction effect is also considered. Compared to the existing extension of K-SVD, the proposed algorithm is more robust to parameters and has better discriminative power for classifying HEp-2 cell patterns. Quantitative evaluation shows that the proposed algorithm outperforms general object classification algorithms significantly on standard HEp-2 cell patterns classifying benchmark1 and also achieves competitive performance on standard natural image classification benchmark.

Related Topics
Physical Sciences and Engineering Computer Science Computer Vision and Pattern Recognition
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