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Encoding features from multi-layer Gabor filtering for visual smoke recognition

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It is a challenging task to accurately recognize smoke from visual scenes due to large variations in smoke shape, color and texture. To improve recognition accuracy, we propose a framework mainly with a robust local feature extraction module based on Gabor convolutional networks. We first propose a Gabor convolutional network, each layer of which mainly consists of Gabor convolution and feature fusion. To fuse feature maps generated by Gabor convolution, we present three Basic Grouping Methods, which divide the feature maps into several groups along orientation axis, scale axis and both of them. To avoid exponential growth of feature maps and preserve discriminative information simultaneously, we propose three element-wise aggregation functions, which are mean, min and max, to combine feature maps in each group. To further improve efficiency, we use local binary pattern to encode hidden and output maps of Gabor convolutional layers. In addition, we use a weight vector to optimize concatenation of histograms for further improvement. Experiments show that our method achieves very outstanding results on smoke, texture and material image datasets. Although the feature extraction step of our method is training-free, our framework consistently outperforms state-of-the-art methods on small smoke datasets, even including deep learning-based methods.

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This work was partially supported by the National Natural Science Foundation of China (61862029), the Open Project of Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering (2018SDSJ01), the Natural Science Foundation of Jiangxi Province (20192BAB207011) and the Science Technology Projects of Jiangxi Education Department (GJJ170317, GJJ170892, GJJ180206).

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Correspondence to Feiniu Yuan or Gang Li.

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Yuan, F., Li, G., Xia, X. et al. Encoding features from multi-layer Gabor filtering for visual smoke recognition. Pattern Anal Applic (2020). https://doi.org/10.1007/s10044-020-00864-x

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  • Smoke recognition
  • Gabor transform
  • Gabor convolutional network
  • Local binary pattern (LBP)
  • Basic grouping method
  • Aggregation function