A robust image classification scheme with sparse coding and multiple kernel learning

Authors:
Dongyang Cheng;Tanfeng Sun;Xinghao Jiang
Affiliations:
School of Information Security Engineering, Shanghai Jiao Tong University, Shanghai, China;School of Information Security Engineering, Shanghai Jiao Tong University, Shanghai, China,National Engineering Lab on Information Content Analysis Techniques, Shanghai, China,Department of Electr ...;School of Information Security Engineering, Shanghai Jiao Tong University, Shanghai, China,National Engineering Lab on Information Content Analysis Techniques, Shanghai, China
Venue:
IWDW'12 Proceedings of the 11th international conference on Digital Forensics and Watermaking
Year:
2012

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Abstract

In recent researches, image classification of objects and scenes has attracted much attention, but the accuracy of some schemes may drop when dealing with complicated datasets. In this paper, we propose an image classification scheme based on image sparse representation and multiple kernel learning (MKL) for the sake of better classification performance. As the fundamental part of our scheme, sparse coding method is adopted to generate precise representation of images. Besides, feature fusion is utilized and a new MKL method is proposed to fit the multi-feature case. Experiments demonstrate that our scheme remarkably improves the classification accuracy, leading to state-of-art performance on several benchmarks, including some rather complicated datasets such as Caltech-101 and Caltech-256.