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Computational Visual Media 2017, 3(1): 83-94 https://doi.org/10.1007/s41095-016-0073-1
Research Article | Open Access | Issue | Published: 17 March 2017

Semi-supervised dictionary learning with label propagation for image classification

Show Author's Information Lin Chen1,  Meng Yang1,2,3( )
College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China.
School of Data and Computer Science, Sun Yat-sen University, Guangzhou, China.
Key Laboratory of Machine Intelligence and Advanced Computing (Sun Yat-sen University), Ministry of Education, China.
Keywords:
image classification, label propagation, semi-supervised learning, dictionary learning
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Chen L, Yang M. Semi-supervised dictionary learning with label propagation for image classification. Computational Visual Media, 2017, 3(1): 83-94. https://doi.org/10.1007/s41095-016-0073-1
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Abstract Full text About this article

Sparse coding and supervised dictionary learning have rapidly developed in recent years, and achieved impressive performance in image classification. However, there is usually a limited number of labeled training samples and a huge amount of unlabeled data in practical image classification, which degrades the discrimination of the learned dictionary. How to effectively utilize unlabeled training data and explore the information hidden in unlabeled data has drawn much attention of researchers. In this paper, we propose a novel discriminative semi-supervised dictionary learning method using label propagation (SSD-LP). Specifically, we utilize a label propagation algorithm based on class-specific reconstruction errors to accurately estimate the identities of unlabeled training samples, and develop an algorithm for optimizing the discriminative dictionary and discriminative coding vectors simultaneously. Extensive experiments on face recognition, digit recognition, and texture classification demonstrate the effectiveness of the proposed method.


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About this article

Semi-supervised dictionary learning with label propagation for image classification

Show Author's information Lin Chen1, Meng Yang1,2,3( )
College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China.
School of Data and Computer Science, Sun Yat-sen University, Guangzhou, China.
Key Laboratory of Machine Intelligence and Advanced Computing (Sun Yat-sen University), Ministry of Education, China.

Abstract

Sparse coding and supervised dictionary learning have rapidly developed in recent years, and achieved impressive performance in image classification. However, there is usually a limited number of labeled training samples and a huge amount of unlabeled data in practical image classification, which degrades the discrimination of the learned dictionary. How to effectively utilize unlabeled training data and explore the information hidden in unlabeled data has drawn much attention of researchers. In this paper, we propose a novel discriminative semi-supervised dictionary learning method using label propagation (SSD-LP). Specifically, we utilize a label propagation algorithm based on class-specific reconstruction errors to accurately estimate the identities of unlabeled training samples, and develop an algorithm for optimizing the discriminative dictionary and discriminative coding vectors simultaneously. Extensive experiments on face recognition, digit recognition, and texture classification demonstrate the effectiveness of the proposed method.

Keywords: image classification, label propagation, semi-supervised learning, dictionary learning

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Publication history

Revised: 04 September 2016
Accepted: 20 December 2016
Published: 17 March 2017
Issue date: March 2017

Copyright

© The Author(s) 2016

Acknowledgements

This work was partially supported by the National Natural Science Foundation for Young Scientists of China (No. 61402289), and the National Science Foundation of Guangdong Province (No. 2014A030313558).

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