HEp-2 cell pattern classification with discriminative dictionary learning
HEp-2 cell pattern classification with discriminative dictionary learning
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DOI：
10.1016/j.patcog.2013.09.025
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发表时间：
2014-07
期刊：
影响因子：
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通讯作者：
Xiangfei Kong;Kuan Li;Jingjing Cao;Qingxiong Yang;Wenyin Liu
中科院分区：
文献类型：
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作者：
Xiangfei Kong;Kuan Li;Jingjing Cao;Qingxiong Yang;Wenyin Liu
提出了一种专门为Hep-2细胞模式分类设计的有监督判别字典学习算法。该算法是对K-SVD算法的扩展：在训练阶段，它考虑了字典原子的区分能力，并在每次更新时减小了它们的类内重构误差。同时，还考虑了它们的类间重构效应。与已有的K-SVD扩展算法相比，该算法对参数的鲁棒性更强，对Hep-2细胞模式的分类具有更好的区分能力。定量评估表明，该算法在标准Hep-2细胞模式分类基准1上的性能明显优于一般目标分类算法，在标准自然图像分类基准上也达到了与之相当的性能。
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 benchmark1and also achieves competitive performance on standard natural image classification benchmark.