Multi-scale bilateral-channels CNN for scene classification

Lei Yuan; Kuangrong Hao; Xuesong Tang; Xin Cai; Yongsheng Ding

doi:10.1117/12.2514957

29 October 2018 Multi-scale bilateral-channels CNN for scene classification

Lei Yuan, Kuangrong Hao, Xuesong Tang, Xin Cai, Yongsheng Ding

Author Affiliations +

Proceedings Volume 10836, 2018 International Conference on Image and Video Processing, and Artificial Intelligence; 108361D (2018) https://doi.org/10.1117/12.2514957
Event: 2018 International Conference on Image, Video Processing and Artificial Intelligence, 2018, Shanghai, China

Abstract

A multi-scale binocular-channels convolution neural network (MBCNN) is proposed to solve complex scene classification and achieved a good accuracy. We use a physiological phenomenon called visual crowding to explain the deficiency of the CNN framework and prove the effectiveness of the double flow model. With the help of a novel bilateral-channels network based on global information and local significant information and our multi-scale feature integration method, the proposed MBCNN can reduce the identification obstacle caused by visual crowding in the V1(Information input area) and V4 (High-level information area) area separately. Experiment results verify that the proposed network has better performance on MIT Indoor 67 and Scene 15 classification datasets.

Citation Download Citation

Lei Yuan, Kuangrong Hao, Xuesong Tang, Xin Cai, and Yongsheng Ding "Multi-scale bilateral-channels CNN for scene classification", Proc. SPIE 10836, 2018 International Conference on Image and Video Processing, and Artificial Intelligence, 108361D (29 October 2018); https://doi.org/10.1117/12.2514957

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KEYWORDS

Visualization

Scene classification

Information fusion

Feature extraction

Information visualization

Convolution

Convolutional neural networks

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