[1]LOWE D G.Distinctive image features from scale-invariant keypoints[J].International Journal of Computer Vision,2004,60(2):91-110.
[2]SUYKENS J A K,VANDEWALLE J.Least squares support vector machine classifiers[J].Neural Processing Letters,1999,9(3):293-300.
[3]DALAL N,TRIGGS B.Histograms of oriented gradients for human detection[C]//Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.Washington D.C.,USA:IEEE Press,2005:886-893.
[4]何希平,张琼华,刘波.基于HOG的目标分类特征深度学习模型[J].计算机工程,2016,42(12):176-180.
[5]FELZENSZWALB P F,GIRSHICK R B,MCALLESTER D,et al.Object detection with discriminatively trained part-based models[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2010,32(9):1627-1645.
[6]REDMON J,DIVVALA S,GIRSHICK R,et al.You only look once:unified,real-time object detection[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.Washington D.C.,USA:IEEE Press,2016:779-788.
[7]HAN J,MORAGA C.The influence of the sigmoid function parameters on the speed of backpropagation learning[C]//Proceedings of International Workshop on Artificial Neural Networks:From Natural to Artificial Neural Computation.Berlin,Germany:Springer-Verlag,1995:195-201.
[8]张蕾,章毅.大数据分析的无限深度神经网络方法[J].计算机研究与发展,2016,53(1):68-79.
[9]KRIZHEVSKY A,SUTSKEVER I,HINTON G E.Imagenet classification with deep convolutional neural networks advances in neural information processing systems[C]//Proceedings of International Conference on Neural Infor-mation Processing System.Washington D.C.,USA:MIT Press,2012:1097-1105.
[10]GRAHAM B.Fractional max-pooling[EB/OL].[2017-03-20].https://arxiv.org/abs/1412.6071.
[11]SRIVASTAVA N,HINTON G E,KRIZHEVSKY A,et al.Dropout:a simple way to prevent neural networks from overfitting[J].Journal of Machine Learning Research,2014,15(1):1929-1958.
[12]SRIVASTAVA N,HINTON G,KRIZHEVSKY A,et al.Dropout:a simple way to prevent neural networks from overfitting[J].Journal of Machine Learning Research,2014,15(1):1929-1958.
[13]HUANG Gao,LIU Zhuang,LAURENS V D M,et al.Weinberger,densely connected convolutional networks[EB/OL].[2017-03-20].https://arxiv.org/abs/1608.06993v3.
[14]SRIVASTAVA R K,GREFF K,Schmidhuber J.Highway Networks[EB/OL].[2017-03-20].https://arxiv.org/abs/150 5.00387.
[15]HE Kaiming,ZHANG Xiangyu,REN Shaoqing,et al.Deep residual learning for image recognition[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.Washington D.C.,USA:IEEE Press,2016:770-778.
[16]SZEGEDY C,LIU Wei,JIA Yangqing,et al.Going deeper with convolutions[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.Washington D.C.,USA:IEEE Press,2015:1-9.
[17]LIU Wei,ANGUELOV D,ERHAN D,et al.SSD:single shot multibox detector[C]//Proceedings of European Conference on Computer Vision.Berlin,Germany:Springer-Verlag,2016:21-37.
[18]SIMONYAN K,ZISSERMAN A.Very deep convolutional networks for large-scale image recognition[EB/OL].[2017-03-20].http://xueshu.baidu.com/s?wd=paperuri%3A%282801f41808e377a1897a3887b6758c59%29&filter=sc_long_sign&tn=SE_xueshusource_2kduw22v≻_vurl=http%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F2014arXiv1409.1556S&ie=utf-8≻_us=4549459500002107255.
[19]SERMANET P,EIGEN D,ZHANG Xiang,et al.Overfeat:integrated recognition,localization and detection using convolutional networks[EB/OL].[2017-03-20].https://arxiv.org/abs/1312.6229v4.
[20]ERHAN D,SZEGEDY C,TOSHEV A,et al.Scalable object detection using deep neural networks[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.Washington D.C.,USA:IEEE Press,2014:2147-2154.
[21]REN Shaoqing,HE Kaiming,GIRSHICK R,et al.Faster R-CNN:towards real-time object detection with region proposal networks[C]//Proceedings of the 28th International Conference on Neural Information Processing Systems.Montreal,Canada:MIT Press,2015:91-99.
|