.png){kind=icon}
.png){kind=icon}
Return
HEp-2 cell pattern classification with discriminative dictionary learning
.png){kind=icon}
PRE
AI
DOI：10.1016/j.patcog.2013.09.025.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
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 benchmark(1) and also achieves competitive performance on standard natural image classification benchmark. (C) 2013 Elsevier Ltd. All rights reserved.
Keywords:
HEp-2 cell classification
Sparse representation
Image coding
Dictionary learning
Image classification
Singular value decomposition
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
7.6
Papers:
1.3W
Citations:
4.5W