Abstract
In image classification tasks, in order to improve the classification accuracy, we need to extract the multidimensional sample characteristics. However, for massive amounts of data, calculation and storage is a big bottleneck. In this paper, a method named image hash was proposed to solve this problem. Image hash can code high-dimensional image feature for simple binary code. Feature extraction is the most important step in image hash. In the current works about image hash, feature extraction needs artificial experience to design feature extractor, which is complicated and not intelligent. Convolution neural network can take original image as input to obtain from the bottom level to the top level of characteristics, which is robust for translation zooming and rotation etc. Therefore, this paper proposes the image hash combined with convolution neural network for image classification, and the experiment proves that it has good classification effect.
This work was supported by the National Natural Science Foundation of China under Grants No. 61571346 and No. 61305041.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
LeCun, Y., Bengio, Y., Hinton, J.: Deep learning. Nature 521, 436–444 (2015). 05/28/print
Dahl, J.V., Koch, K.C., Kleinhans, E., et al.: Convolutional networks and applications in vision. C. In: IEEE International Symposium on Circuits & Systems, pp. 253–256. IEEE Press (2010)
Zhaoqing, L., Qiong, L., Jingrui, L., Xiyuan, P.: SIFT based image hashing algorithm. Chin. J. Sci. Instrum. 32(9), 2024–2028 (2011)
LeCun, Y., Boser, B., Denker, J.S., Henderson, D., Howard, R.E., Hubbard, W., Jackel, L.D.: Backpropagation applied to handwritten zip code recognition. Neural Comput. 1, 541–551 (1989)
Fu, H.: Research on Hashing—Based Image Retrieval for Large—ScaleDataset. Dalian University of Technology (2014)
Ciresan, D.C., Meier, U., Masci, J., Maria Gambardella, L., Schmidhuber, J.: Flexible, high performance convolutional neural networks for image classification. In: IJCAI Proceedings-International Joint Conference on Artificial Intelligence, p. 1237 (2011)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)
Ciresan, D.C., Meier, U., Schmidhuber, J.: Multi-column deep neural networks for image classification. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3642–3649 (2012)
Fu, Y., Wang, Y.W., Wang, W.Q., Gao, W.: Content-based natural image classification and retrieval using SVM. Chin. J. Comput. 26, 1261–1265 (2003)
Lecun, Y., Bottou, L., Bengio, Y., et al.: Gradient-based learning applied to document recognition. J. Proc. IEEE 86(11), 2278–2324 (1998)
Nair, V., Hinton, G.E.: Rectified linear units improve restricted boltzmann machines. In: Proceedings of 27th International Conference on Machine Learning (2010)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Chen, Y., Yan, Y., Zhao, D. (2017). Image Classification Based on Image Hash Convolution Neural Network. In: Pan, JS., Snášel, V., Sung, TW., Wang, X. (eds) Intelligent Data Analysis and Applications. ECC 2016. Advances in Intelligent Systems and Computing, vol 535. Springer, Cham. https://doi.org/10.1007/978-3-319-48499-0_8
Download citation
DOI: https://doi.org/10.1007/978-3-319-48499-0_8
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-48498-3
Online ISBN: 978-3-319-48499-0
eBook Packages: EngineeringEngineering (R0)