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.
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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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Lin Chen received his B.S. degree in computer science and technology from Shenzhen University, Shenzhen, China, in 2015. He is currently pursuing his M.S. degree in the School of Computer Science & Software Engineering, Shenzhen University.
Meng Yang is currently an associate professor in the School of Computer Science & Software Engineering, Shenzhen University, Shenzhen, China. He received his Ph.D. degree from Hong Kong Polytechnic University, Hong Kong, China, in 2012. Before joining Shenzhen University, he worked as a postdoctoral fellow in the Computer Vision Lab. of ETH Zurich. His research interests include sparse coding, dictionary learning, object recognition, and machine learning. He has published 10 AAAI/CVPR/ICCV/ICML/ECCV papers, and several IJCV, IEEE TNNLS, and TIP journal papers.
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Chen, L., Yang, M. Semi-supervised dictionary learning with label propagation for image classification. Comp. Visual Media 3, 83–94 (2017). https://doi.org/10.1007/s41095-016-0073-1
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DOI: https://doi.org/10.1007/s41095-016-0073-1