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End-to-End Trained Sparse Coding Network with Spatial Pyramid Pooling for Image Classification

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Abstract

Spatial pyramid matching using sparse coding (ScSPM) has become an efficient method and a benchmark in image classification. However, since it is unsupervised, the trained dictionary may be suboptimal. To further improve classification accuracy, in this paper we propose a sparse coding network with spatial pyramid pooling based on the end-to-end deep learning approach. In our new system, the minimization problem in sparse coding can be modeled as a feed-forward neural network and image features can be extracted by the deep convolutional network. By minimizing the final classifier loss using the end-to-end deep learning method, the sparse coding network can be trained in a supervised way. Our proposed model is tested on three image databases and in terms of classification accuracy, it significantly outperforms ScSPM. Compared with other image classification approaches based on deep learning, it can also achieve a noticeable improvement.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (Nos. 61302055, 61401160, 61327005), the Science and Technology Planning Project of Guangdong Province (2017A020214011), the Funds for the Central Universities 2017MS039, the Guangdong Provincial Key Laboratory of Short-Range Wireless Detection and Communication (No. 2014B030301010, 2017B030314003), the Science and Technology Program of Guangzhou (No. 201804020079) and the Project sponsored by SRF for ROCS, SEM.

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Authors and Affiliations

  1. National Engineering Technology Research Center for Mobile Ultrasonic Detection, School of Electronics and Information Engineering, South China University of Technology, Guangzhou, People’s Republic of China

    Boheng Chen, Yige Wang, Gang Wei, Jie Li & Biyun Ma

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  1. Boheng Chen
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  2. Yige Wang
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  3. Gang Wei
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  4. Jie Li
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Correspondence to Boheng Chen.

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Chen, B., Wang, Y., Wei, G. et al. End-to-End Trained Sparse Coding Network with Spatial Pyramid Pooling for Image Classification. Neural Process Lett 50, 2021–2036 (2019). https://doi.org/10.1007/s11063-018-9967-5

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