Typhoon Classification Model Based on Multi-Scale Convolution Feature Fusion

Peng Lu; Peiqi Zou; Guoliang Zou

doi:10.3788/LOP56.160101

[1] Huang S P, Yu L, Feng Z Y[J]. A survey on image categorization using supervector coding Electronic Technology & Software Engineering, 2013, 32-37.

[2] Krizhevsky A, Sutskever I, Hinton G E. ImageNet classification with deep convolutional neural networks[J]. Communications of the ACM, 60, 84-90(2017).

[3] Huang H, He K, Zheng X L et al. Spatial-spectral feature extraction of hyperspectral image based on deep learning[J]. Laser & Optoelectronics Progress, 54, 101001(2017).

[4] LeCun Y, Bottou L, Bengio Y et al. . Gradient-based learning applied to document recognition[J]. Proceedings of the IEEE, 86, 2278-2324(1998).

[5] Zeiler M D, Fergus R. Visualizing and understanding convolutional networks[M]. ∥ Fleet D, Pajdla T, Schiele B, et al. Computer vision-ECCV 2014. Lecture notes in computer science. Cham:Springer, 8689, 818-833(2014).

[6] Luo J H, Wu J X. A survey on fine-grained image categorization using deep convolutional features[J]. Acta Automatica Sinica, 43, 1306-1318(2017).

[7] Wang L L, Liu J H, Fu X M. Facial expression recognition based on fusion of local features and deep belief network[J]. Laser & Optoelectronics Progress, 55, 011002(2018).

[8] Niu L Q, Chen X Z, Zhang S N et al. Model construction and performance analysis for deep consecutive convolutional neural network[J]. Journal of Shenyang University of Technology, 38, 662-666(2016).

[9] Cui R X, Liu M Y, Liu M H. Facial expression recognition based on ensemble of mulitple CNNs[M]. ∥ You Z S, Zhou J, Wang Y H, et al. Biometric recognition. Lecture notes in computer science. Cham: Springer, 9967, 511-518(2016).

[10] Goodfellow I J, Le Q V, Saxe A M et al. Measuring invariances in deep networks. [C]∥Proceedings of the 22nd International Conference on Neural Information Processing Systems, December 7-10, 2009, Vancouver, British Columbia, Canada. USA: Curran Associates Inc., 646-654(2009).

[11] Gu J X, Wang Z H, Kuen J et al. Recent advances in convolutional neural networks[J]. Pattern Recognition, 77, 354-377(2018).

[12] Zeiler M D, Krishnan D, Taylor G W et al. Deconvolutional networks. [C]∥2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, June 13-18, 2010, San Francisco, CA, USA. New York: IEEE, 2528-2535(2010).

[13] Wang M, Hao J, Yao C H et al. Sign language semantic recognition based on optimized fully convolutional neural network[J]. Laser & Optoelectronics Progress, 55, 111010(2018).

[14] Ivanovsky L, Khryashchev V, Lebedev A et al. Facial expression recognition algorithm based on deep convolution neural network. [C]∥2017 21st Conference of Open Innovations Association (FRUCT), November 6-10, 2017, Helsinki,Finland. New York: IEEE, 141-147(2018).

[15] Chen H J, Wang Q Q, Yang G W et al. SSD object detection algorithm with multi-scale convolution feature fusion[J]. Journal of Frontiers of Computer Science and Technology, 13, 1049-1061(2019).

[16] He K M, Zhang X Y, Ren S Q et al. Spatial pyramid pooling in deep convolutional networks for visual recognition[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 37, 1904-1916(2015). http://www.sciencedirect.com/science/article/pii/S0031320315004252

[17] Lee S H, Chan C S, Mayo S J et al. How deep learning extracts and learns leaf features for plant classification[J]. Pattern Recognition, 71, 1-13(2017).

[18] Iwahori Y, Takada Y, Shiina T et al. Defect classification of electronic board using dense SIFT and CNN[J]. Procedia Computer Science, 126, 1673-1682(2018).