Abstract
Image classification is a challenging issue in pattern recognition due to complex interior structure for data. Meanwhile, the high-dimension data leads to heavy computational burden. To overcome these shortcomings, in this paper, we learn a discriminative projection and representation in a unified framework for image classification task. This method seeks a discriminative representation in a low-dimension space for an image, which enhances the classification accuracy and efficiency. Thus, a projection matrix is learnt by a criterion which demands the minimum of within-class residual and maximum of between-class residual in an iterative procedure. Then all samples are projected into a low-dimension space, and obtain the discriminative representation via L2 regularization. The experimental results demonstrate that the proposed method achieves better classification performances, compared with state-of-the-art sparse representation methods.
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Acknowledgements
This work was supported in part by the Natural Science Foundation of China under Grant 61703283, 61773328, 61672358, 61703169, 61573248, in part by the research grant of the Hong Kong Polytechnic University (Project Code: G-UA2B), in part by the China Postdoctoral Science Foundation under Project 2016M590812, Project 2017T100645 and Project 2017M612736, in part by the Guangdong Natural Science Foundation under Project 2017A030310067, Project with the title Rough Sets-Based Knowledge Discovery for Hybrid Labeled Data and Project with the title The Study on Knowledge Discovery and Uncertain Reasoning in Multi-Valued Decisions.
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Zhong, Z., Wen, J., Gao, C., Zhou, J. (2019). Learning a Discriminative Projection and Representation for Image Classification. In: Wong, W. (eds) Artificial Intelligence on Fashion and Textiles. AITA 2018. Advances in Intelligent Systems and Computing, vol 849. Springer, Cham. https://doi.org/10.1007/978-3-319-99695-0_22
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DOI: https://doi.org/10.1007/978-3-319-99695-0_22
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