Abstract
In the last few decades, the constant growth of digital images, as the main source of information representation for scientific applications, has made image classification a challenging task. To achieve high classification yields, different pattern recognition techniques have been proposed, among which are the deep learning methods that today focus their study on image processing and computer vision. In this approach, the most popular architecture for the image classification task is the convolutional neural network (CNN), a network constructed of multiple layers and where each layer models a receptive field of the visual cortex making it much more effective in artificial vision tasks [1]. This paper proposes a convolutional network architecture with a performance-enhancing approach, a hierarchical structure that is easy to build, adaptive, and easy to train with good performance in image classification tasks.
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
Wang, H., Ding, S., Wu, D., Zhang, Y., Yang, S.: Smart connected electronic gastroscope system for gastric cancer screening using multi-column convolutional neural networks. Int. J. Prod. Res. 57(21), 6795–6806 (2019)
Wang, Y., Hu, S., Wang, G., Chen, C., Pan, Z.: Multi-scale dilated convolution of convolutional neural network for crowd counting. Multimedia Tools Appl. 79(1), 1057–1073 (2019). https://doi.org/10.1007/s11042-019-08208-6
Li, Z., Zhou, A., Shen, Y.: An end-to-end trainable multi-column CNN for scene recognition in extremely changing environment. Sensors 20(6), 1556 (2020)
Hu, Y., Lu, M., Lu, X.: Feature refinement for image-based driver action recognition via multi-scale attention convolutional neural network. Sig. Process. Image Commun. 81, 115697 (2020)
LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521, 436–444 (2015)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)
CireşAn, D., Meier, U., Masci, J., Schmidhuber, J.: Multi-column deep neural network for traffic sign classification. Neural Netw. 32, 333–338 (2012)
Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., Darrell, T.: Caffe: convolutional architecture for fast feature embedding. In: Proceedings of the 22nd ACM International Conference on Multimedia, pp. 675–678. ACM (2014)
Viloria, A., Acuña, G.C., Franco, D.J.A., Hernández-Palma, H., Fuentes, J.P., Rambal, E.P.: Integration of data mining techniques to PostgreSQL database manager system. Procedia Comput. Sci. 155, 575–580 (2019)
Yu, D., Wang, H., Chen, P., Wei, Z.: Mixed pooling for convolutional neural networks. In: International Conference on Rough Sets and Knowledge Technology, pp. 364–375. Springer, Cham (2014)
Varela, N., Silva, J., Gonzalez, F.M., Palencia, P., Palma, H.H., Pineda, O.B.: Method for the recovery of images in databases of rice grains from visual content. Procedia Comput. Sci. 170, 983–988 (2020)
Ciregan, D., Meier, U., Schmidhuber, J.: Multi-column deep neural networks for image classification. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3642–3649. IEEE (2012)
Oliva, T.: http://cvcl.mit.edu/database.htm, May 2016
Khosla, A., Nityananda, J., Yao, B., Fei-Fei, L.: Stanford Dogs Dataset, September 2017. http://vision.stanford.edu/aditya86/ImageNetDogs/
Caltech256: Caltech 256 Dataset, May 2016. www.vision.caltech.edu/ImageDatasets/Caltech256
Zhang, Y., Zhou, D., Chen, S., Gao, S., Ma, Y.: Single-image crowd counting via multi-column convolutional neural network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 589–597 (2016)
Cireşan, D., Meier, U.: Multi-column deep neural networks for offline handwritten Chinese character classification. In: 2015 International Joint Conference on Neural Networks (IJCNN), pp. 1–6. IEEE (2015)
Lu, X., Lin, Z., Shen, X., Mech, R., Wang, J.Z.: Deep multi-patch aggregation network for image style, aesthetics, and quality estimation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 990–998 (2015)
Ke, Q., Ming, L.D., Daxing, Z.: Image steganalysis via multi-column convolutional neural network. In: 2018 14th IEEE International Conference on signal processing (ICSP), pp. 550–553. IEEE (2018)
McDonnell, M.D., Vladusich, T.: Enhanced image classification with a fast-learning shallow convolutional neural network. In: 2015 International Joint Conference on Neural Networks (IJCNN), pp. 1–7. IEEE (2015)
Viloria, A., Lezama, O.B.P.: Improvements for determining the number of clusters in k-Means for innovation databases in SMEs. ANT/EDI40, pp. 1201–1206 (2019)
Yang, W., Jin, L., Xie, Z., Feng, Z.: Improved deep convolutional neural network for online handwritten Chinese character recognition using domain-specific knowledge. In: 2015 13th International Conference on Document Analysis and Recognition (ICDAR), pp. 551–555. IEEE (2015)
Jmour, N., Zayen, S., Abdelkrim, A.: Convolutional neural networks for image classification. In: 2018 International Conference on Advanced Systems and Electric Technologies (IC_ASET), pp. 397–402. IEEE (2018)
Park, T., Lee, T.: Musical instrument sound classification with deep convolutional neural network using feature fusion approach (2015). arXiv preprint arXiv:1512.07370
Zhong, Z., Jin, L., Xie, Z.: High performance offline handwritten Chinese character recognition using Googlenet and directional feature maps. In: 2015 13th International Conference on Document Analysis and Recognition (ICDAR), pp. 846–850. IEEE (2015)
Du, J., Zhai, J.F., Hu, J.S., Zhu, B., Wei, S., Dai, L.R.: Writer adaptive feature extraction based on convolutional neural networks for online handwritten Chinese character recognition. In: 2015 13th International Conference on Document Analysis and Recognition (ICDAR), pp. 841–845. IEEE (2015)
Bindhu, V.: Biomedical image analysis using semantic segmentation. J. Innov. Image Process. (JIIP) 1(02), 91–101 (2019)
Sharma, N., Jain, V., Mishra, A.: An analysis of convolutional neural networks for image classification. Procedia Comput. Sci. 132, 377–384 (2018)
Yim, J., Sohn, K.A.: Enhancing the performance of convolutional neural networks on quality degraded datasets. In: 2017 International Conference on Digital Image Computing: Techniques and Applications (DICTA), pp. 1–8. IEEE (2017)
Verma, A., Vig, L.: Using convolutional neural networks to discover cogntively validated features for gender classification. In: 2014 International Conference on Soft Computing and Machine Intelligence, pp. 33–37. IEEE (2014)
Zeng, Y., Xu, X., Fang, Y., Zhao, K.: Traffic sign recognition using extreme learning classifier with deep convolutional features. In: The 2015 International Conference on Intelligence Science and Big Data Engineering (IScIDE 2015), Suzhou, China, vol. 9242, pp. 272–280 (2015)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Silva, J., Varela, N., Patiño-Saucedo, J.A., Lezama, O.B.P. (2021). Convolutional Neural Network with Multi-column Characteristics Extraction for Image Classification. In: Chen, J.IZ., Tavares, J.M.R.S., Shakya, S., Iliyasu, A.M. (eds) Image Processing and Capsule Networks. ICIPCN 2020. Advances in Intelligent Systems and Computing, vol 1200. Springer, Cham. https://doi.org/10.1007/978-3-030-51859-2_3
Download citation
DOI: https://doi.org/10.1007/978-3-030-51859-2_3
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-51858-5
Online ISBN: 978-3-030-51859-2
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)