HEp-2 cell pattern classification with discriminative dictionary learning

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

23 Scopus Citations
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Author(s)

  • Xiangfei Kong
  • Kuan Li
  • Jingjing Cao
  • Qingxiong YANG
  • Wenyin Liu

Related Research Unit(s)

Detail(s)

Original languageEnglish
Pages (from-to)2379-2388
Journal / PublicationPattern Recognition
Volume47
Issue number7
Online published3 Oct 2013
Publication statusPublished - Jul 2014

Abstract

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.

Research Area(s)

  • Dictionary learning, HEp-2 cell classification, Image classification, Image coding, Singular value decomposition, Sparse representation

Citation Format(s)

HEp-2 cell pattern classification with discriminative dictionary learning. / Kong, Xiangfei; Li, Kuan; Cao, Jingjing et al.
In: Pattern Recognition, Vol. 47, No. 7, 07.2014, p. 2379-2388.

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review