1. 毕业设计（论文）的内容和要求
当下是一个大数据的时代，我们每个人都参与其中。
在大数据时代，将数据有效的检索并组织呈现出来有着很重要的意义。
而为了实现这个，图像识别技术是不可缺少的一部分。
2. 参考文献
[1] 基于角点的图像特征提取与匹配算法研究，薛金龙，2014.[2] 基于局部特征的图像匹配与识别，宫明明，2014.[3] 基于视觉信息的图像特征提取算法研究，戴金波，2014.[4] Friedman J, Hastie T, Tibshirani R. The elements of statistical learning[M]. Springer, Berlin: Springer series in statistics, 2001.[5] D. G. Lowe, Distinctive image features from scale-invariant keypoints. IJCV, 60(2):91-110, 2004.[6] N. Dalal, B. Triggs, Histograms of Oriented Gradients for Human Detection, Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2005.[7] Ahonen, T., Hadid, A., and Pietikinen, M. (2006). Face description with local binary patterns: Application to face recognition. PAMI, 28.[8] J. Sivic, A. Zisserman, Video Google: A Text Retrieval Approach to Object Matching in Videos, Proc. Ninth Int'l Conf. Computer Vision, pp. 1470-1478, 2003.[9] B. Olshausen, D. Field, Sparse Coding with an Overcomplete Basis Set: A Strategy Employed by V1?, Vision Research, vol. 37, pp. 3311-3325, 1997.[10] Wang, J., Yang, J., Yu, K., Lv, F., Huang, T., and Gong, Y. (2010). Locality-constrained Linear Coding for image classification. In CVPR.[11] Perronnin, F., Snchez, J., Mensink, T. (2010). Improving the fisher kernel for large-scale image classification. In ECCV (4).[12] Lin, Y., Lv, F., Cao, L., Zhu, S., Yang, M., Cour, T., Yu, K., and Huang, T. (2011). Large-scale image clas- sification: Fast feature extraction and SVM training. In CVPR.[13] Krizhevsky, A., Sutskever, I., and Hinton, G. (2012). ImageNet classification with deep convolutional neu- ral networks. In NIPS.[14] G.E. Hinton, N. Srivastava, A. Krizhevsky, I. Sutskever, and R.R. Salakhutdinov. Improving neural networks by preventing co-adaptation of feature detectors. arXiv preprint arXiv:1207.0580, 2012.[15] K. Chatfield, K. Simonyan, A. Vedaldi, A. Zisserman. Return of the Devil in the Details: Delving Deep into Convolutional Nets. BMVC, 2014。
[16] Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A., Going deeper with convolutions. In: CVPR. (2015)[17] Lin, M., Chen, Q., and Yan, S. Network in network. In Proc. ICLR, 2014.[18] S. Ioffe and C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. In ICML, 2015.[19] K. He, X. Zhang, S. Ren, J. Sun. Deep Residual Learning for Image Recognition. CVPR 2016.[20] Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z. Rethinking the incep-tion architecture for computer vision. In: CVPR. (2016).[21] Szegedy, C., Ioffe, S., Vanhoucke, V. Inception-v4, inception-resnet and the impact of residual connections on learning. arXiv:1602.07261 (2016).[22] Everingham, M., Eslami, S. M. A., Van Gool, L., Williams, C. K. I., Winn, J. and Zisserman, A. The Pascal Visual Object Classes Challenge: A Retrospective. International Journal of Computer Vision, 111(1), 98-136, 2015.
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