Application Research of Deep Convolutional Neural Network in Computer Vision
Abstract
As an important research achievement in the field of brain like computing, deep convolution neural network has been widely used in many fields such as computer vision, natural language processing, information retrieval, speech recognition, semantic understanding and so on. It has set off a wave of neural network research in industry and academia and promoted the development of artificial intelligence. At present, the deep convolution neural network mainly simulates the complex hierarchical cognitive laws of the human brain by increasing the number of layers of the network, using a larger training data set, and improving the network structure or training learning algorithm of the existing neural network, so as to narrow the gap with the visual system of the human brain and enable the machine to acquire the capability of “abstract concepts”. Deep convolution neural network has achieved great success in many computer vision tasks such as image classification, target detection, face recognition, pedestrian recognition, etc. Firstly, this paper reviews the development history of convolutional neural networks. Then, the working principle of the deep convolution neural network is analyzed in detail. Then, this paper mainly introduces the representative achievements of convolution neural network from the following two aspects, and shows the improvement effect of various technical methods on image classification accuracy through examples. From the aspect of adding network layers, the structures of classical convolutional neural networks such as AlexNet, ZF-Net, VGG, GoogLeNet and ResNet are discussed and analyzed. From the aspect of increasing the size of data set, the difficulties of manually adding labeled samples and the effect of using data amplification technology on improving the performance of neural network are introduced. This paper focuses on the latest research progress of convolution neural network in image classification and face recognition. Finally, the problems and challenges to be solved in future brain-like intelligence research based on deep convolution neural network are proposed.
References
Hubel DH, Wiesel TN. Receptive files, binocular interaction and functional architecture in the cat’s visual cortex. The Journal of Physiology 1692; 160(1): 106-154.
Waibel A, Hanazawa T, Hinton G, et al. Phonemere recognition using time-delay neural network. Acoustics, Speech and Signal Processing, IEEE Transaction 1989; 37(3): 328-339.
Lecun Y, Bottou L, Bengio Y, et al. Gradient-based learning applied to document recognition. Proceedings of the IEEE 1998; 86(11): 2278-2324.
Li F, Fergus R, Perona P. Learning generative visual models from few training examples: An incremental Bayesian approach tested on 101 object categories. Computer Visionand Image Understanding 2007; 106(1): 59-70.
Torralba A, Fergus R, Freeman WT. 80 million tiny images: A large data set for nonparametric object and scene recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 2008; 30(11): 1958-1970.
Xiao J, Hays J, Ehinger KA, et al. Sun database: Large-scale scene recognition from abbey to zoo. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition can Francisco; USA; 2010. p. 3485-3492.
Deng J, Dong W, Socher R, et al. Imagenet: A large-scale hierarchical image database. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition; Miami, USA; 2009. p. 248-255.
Huang GB, Mattar M, Berg T, et al. Labeled faces in the wild: A database for studying face recognition in unconstrained environments. University of Massachusetts, Amherst. USA Technical Report 2008; 07-49.
Kumar N, Berg AC, Belhumeur PN, et al. Attribute and simile classifiers for face verification. Proceedings of the International Conference on Computer Vision; Kyoto, Japan; 2009. p. 365-372.
Chen D. Cao X, Wen F, et al. Blessing of dimensionality high dimensional feature and its efficient compression for face verification. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition; Portland, USA; 2013. p. 3025-3032.
Sun Y, Wang X, Tang X. Hybrid deep learning for face verification. Proceedings of the IEEE International Conference on Computer Vision; Sydney, Australia; 2013. p. 1489-1496.
Sun Y, Wang X, Tang X. Deep learning face representation from predicting 10,000 classes. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Columbus; USA; 2014. p. 1891-1898.
Taigman Y, Yang M. Ranzato M, et al. Deepface: Closing the gap to humarr level performance in faceverification. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition; Columbus, USA; 2014. p. 1701-1708.
Sun Y, Chen Y, Wang X, et al. Deep learning face repre-sentation by joint identificatior-verification. Proceedings of the Advances in Neural Information Processing Systems Montreal; Canada; 2014. p. 1988-1996.
Copyright (c) 2020 Journal of Networking and Telecommunications
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Authors contributing to this journal agree to publish their articles under the Creative Commons Attribution-Noncommercial 4.0 International License, allowing third parties to share their work (copy, distribute, transmit) and to adapt it, under the condition that the authors are given credit, that the work is not used for commercial purposes, and that in the event of reuse or distribution, the terms of this license are made clear. With this license, the authors hold the copyright without restrictions and are allowed to retain publishing rights without restrictions as long as this journal is the original publisher of the articles.
