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Elastic net regularized dictionary learning for image classification

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Abstract

Dictionary learning plays a key role in image representation for classification. A multi-modal dictionary is usually learned from feature samples across different classes and shared in the feature encoding process. Ideally each atom in dictionary corresponds to a single class of images, while each class of images corresponds to a certain group of atoms. Image features are encoded as linear combinations of selected atoms in a given dictionary. We propose to use elastic net as regularizer to select atoms in feature coding and related dictionary learning process, which not only benefits from the sparsity similar as ℓ 1 penalty but also encourages a grouping effect that helps improve image representation. Experimental results of image classification on benchmark datasets show that with dictionary learned in the proposed way outperforms state-of-the-art dictionary learning algorithms.

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Notes

  1. All the results of OCSVM and HIKVQ are based on step size 8 and without concatenated Sobel images.

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Acknowledgments

This work was supported by the National Natural Science Foundation of P.R. China (No. 61402535), Qingdao Science and Technology Project (No. 14-2-4-111-jch), the Fundamental Research Funds for the Central Universities (No. R1405012A), and the Talent Acquisition Project (No.Y1305024).

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Authors and Affiliations

  1. Department of Computer Science, Purdue University, West Lafayette, IN, 47907, USA

    Bin Shen & Qifan Wang

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

    Bao-Di Liu

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  1. Bin Shen
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  2. Bao-Di Liu
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  3. Qifan Wang
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Correspondence to Bin Shen or Bao-Di Liu.

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Shen, B., Liu, BD. & Wang, Q. Elastic net regularized dictionary learning for image classification. Multimed Tools Appl 75, 8861–8874 (2016). https://doi.org/10.1007/s11042-014-2257-y

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