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An Image Mosaic Method Based on Convolutional Neural Network Semantic Features Extraction

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Abstract

Since traditional image feature extraction methods rely on features such as corner points, a new method based on semantic feature extraction is proposed inspiring by convolution neural attack. The semantic features of each pixel in an image are computed and quantified by neural network to represent the contribution of each pixel to the image semantics. According to the quantization results, the semantic contribution values of each pixel are sorted, and the semantic feature points are selected from high to low and the image mosaic is completed. Experimental results show that this method can effectively extract image features and complete image mosaic.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (NO.61674115), and the Natural Science Foundation of Tianjin City, China (No.17JCYBJC15900).

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Authors and Affiliations

  1. School of Microelectronics, Tianjin University, 92 Weijin Road, Nankai District, Tianjin, 300072, China

    Zaifeng Shi, Hui Li, Huizheng Ren & Boyu Fan

  2. Tianjin Key Lab of Imaging & Sensing Microelectronics Technology, Tianjin, China

    Zaifeng Shi

  3. School of Mathematical Sciences, Tianjin Normal University, Tianjin, China

    Qingjie Cao

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  1. Zaifeng Shi
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  2. Hui Li
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  3. Qingjie Cao
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  4. Huizheng Ren
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  5. Boyu Fan
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Correspondence to Zaifeng Shi.

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Shi, Z., Li, H., Cao, Q. et al. An Image Mosaic Method Based on Convolutional Neural Network Semantic Features Extraction. J Sign Process Syst 92, 435–444 (2020). https://doi.org/10.1007/s11265-019-01477-2

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  • DOI: https://doi.org/10.1007/s11265-019-01477-2

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