[1] ITTI L, KOCH C, NIEBUR E. A model of saliency-based visual attention for rapid scene analysis［J］.IEEE Tran-sactions on Pattern Analysis & Machine Intelligence, 1998, 20(11):1254-1259.
[2] SCH, LKOPF B, PLATT J, HOFMANN T.Graph-based visual saliency［J］.Advances in Neural Information Processing Systems, 2007, 19:545-552.
[3] HOU X, ZHANG L. Saliency detection:a spectral residual approach［C］//IEEE Conference on Computer Vision and Pattern Recognition, 2007:1-8.
[4] ACHANTA R, HEMAMI S, ESTRADA F, et al. Frequency-tuned salient region detection［C］//IEEE Conference on Computer Vision and Pattern Recognition, 2009:1597-1604.
[5] GUO C L, MA Q, ZHANG L M.Spatio-temporal saliency detection using phase spectrum of quaternion Fourier transform［C］//IEEE Conference on Computer Vision and Pattern Recognition2008:1-8.
[7] WANG X, LV Q, WANG B, et al. Airport detection in remote sensing images: a method based on saliency map.［J］.Cognitive Neurodynamics, 2013, 7(2):143-54.
[10] THEODORIDIS S.Chapter 18-neural networks and deep learning［M］//Machine Learning.Amsterdam：Elsevier Ltd, 2015:875-936.
[11] SZEGEDY C, VANHOUCKE V, IOFFE S, et al.Rethinking the inception architecture for computer vision［C］//IEEE Conference on Computer Vision and Pattern Recognition（CVPR）, 2016.doi:10.1109/CVPR.2016.308.
[12] SZEGEDY C, IOFFE S, VANHOUCKE V, et al. Inception-v4, inception-ResNet and the impact of residual connections on learning［EB/OL］.［2017-05-07］.https://arxiv.org/pdf/1409.1556.pdf.
[13] SIMONYAN K, ZISSERMAN A.Very deep convolutional networks for large-scale image recognition［EB/OL］.［2017-05-18］.https://arxiv.org/pdf/1602.07261.pdf.
[14] HE K, ZHANG X Y, REN S Q, et al.Deep residual learning for image recognition［C］//IEEE Conference on Computer Vision and Pattern Recognition, 2015:770-778.
[15] KRIZHEVSKY A, SUTSKEVER I, HINTON G E. ImageNet classification with deep convolutional neural networks［C］//International Conference on Neural Information Processing Systems, 2012:1097-1105.
[16] CLEVERT D A, UNTERTHINER T, HOCHREITER S. Fast and accurate deep network learning by Exponential Linear Units (ELUs)［C］//ICLR, 2016:1-14.
[17] HE K M, ZHANG X YvREN S Q, et al.Delving deep into rectifiers:surpassing human-level performance on imageNet classification［J］.IEEE International Conference on Computer Vision(ICCV), 2015:1026-1034.
[18] XU B, WANG N Y, CHEN T Q, et al.Empirical evaluation of rectified activations in convolutional network［EB/OL］.［2017-06-16］.https://arxiv.org/pdf/1505.00853.pdf.
[19] HINTON G E, SRIVASTAVA N, KRIZHEVSKY A, et al.Improving neural networks by preventing co-adaptation of feature detectors［J］.Computer Science, 2012, 3(4):212-223.
