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Sparse representation for image classification via paired dictionary learning

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Abstract

Sparse coding technique is usually applied for feature representation. To learn discriminative features for visual recognition, a dictionary learning method, called Paired Discriminative K-SVD (PD-KSVD), is presented in this paper. Firstly, to reduce the reconstruction error of positive class while increasing the errors of negative classes, the scheme inverted signal is applied to the negative training samples. Then, the class-specific sub-dictionaries are learned from pairs of positive and negative classes to jointly achieve high discrimination and low reconstruction errors for sparse coding. Multiple sub-dictionaries are concatenated with respect to the same negative class so that the non-zero sparse coefficients can be discriminatively distributed to improve classification accuracy. Last, sparse coefficients are solved via the concatenated sub-dictionaries and used to train the classifier. Compared to the existing dictionary learning methods, PD-KSVD method achieves superior performance in a variety of visual recognition tasks on several publicly available datasets.

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Authors and Affiliations

  1. Center for Innovative Research on Aging Society (CIRAS), Advanced Institute of Manufacturing with High-tech Innovations and Department of Computer Science and Information Engineering, National Chung Cheng University, No. 168, Sec. 1, University Rd., Min-Hsiung Township, Chiayi, Taiwan

    Hui-Hung Wang, Chia-Wei Tu & Chen-Kuo Chiang

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  1. Hui-Hung Wang
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  2. Chia-Wei Tu
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  3. Chen-Kuo Chiang
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Correspondence to Chen-Kuo Chiang.

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Wang, HH., Tu, CW. & Chiang, CK. Sparse representation for image classification via paired dictionary learning. Multimed Tools Appl 78, 16945–16963 (2019). https://doi.org/10.1007/s11042-018-6888-2

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