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Discriminative sparse neighbor coding

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Abstract

Sparse coding has received extensive attention in the literature of image classification. Traditional sparse coding strategies tend to approximate local features in terms of a linear combination of basis vectors, without considering feature neighboring relationships. In this scenario, similar instances in the feature space may result in totally different sparse codes. To address this shortcoming, we investigate how to develop new sparse representations which preserve feature similarities. We commence by establishing two modules to improve the discriminative ability of sparse representation. The first module selects discriminative features for each class, and the second module eliminates non-informative visual words. We then explore the distribution of similar features over the dominant basis vectors for each class. We incorporate the feature distribution into the objective function, spanning a class-specific low dimensional subspace for effective sparse coding. Extensive experiments on various image classification tasks validate that the proposed approach consistently outperforms several state-of-the-art methods.

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Notes

  1. http://www-cvr.ai.uiuc.edu/ponce_grp/data/scene_categories/scene_categories.zip

  2. http://vision.stanford.edu/lijiali/event_dataset/

  3. http://www.vision.caltech.edu/Image_Datasets/Caltech101/

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Acknowledgments

This work was supported by NSFC projects (No. 61370123 and 61503422), Shandong Outstanding Young Scientist Fund (No.BS2013DX006), Qingdao Fundamental Research Project (No. 13-1-4-256-jch), and the Australian Research Councils DECRA Projects funding scheme (project ID DE120102948).

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Authors and Affiliations

  1. School of Computer Science and Engineering, Beihang University, Beijing, 100191, China

    Xiao Bai & Cheng Yan

  2. College of Information and Control Engineering, China University of Petroleum (Huadong), Qingdao, 266580, China

    Peng Ren

  3. School of Information, Central University of Finance and Economics, Beijing, 100081, China

    Lu Bai

  4. School of Information and Communication Technology, Griffith University, Nathan, QLD, 4111, Australia

    Jun Zhou

Authors
  1. Xiao Bai
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  2. Cheng Yan
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  3. Peng Ren
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  4. Lu Bai
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  5. Jun Zhou
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Correspondence to Peng Ren.

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Cite this article

Bai, X., Yan, C., Ren, P. et al. Discriminative sparse neighbor coding. Multimed Tools Appl 75, 4013–4037 (2016). https://doi.org/10.1007/s11042-015-2951-4

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