Effects of Varying Resolution on Performance of CNN based Image Classification: An Experimental Study

Authors

  • Kannojia SP ICT Research Lab, Department of Computer Science, University of Lucknow, Lucknow, India
  • Jaiswal G ICT Research Lab, Department of Computer Science, University of Lucknow, Lucknow, India

DOI:

https://doi.org/10.26438/ijcse/v6i9.451456

Keywords:

Varying Resolution, Convolution Neural Network, Image Classification, Feature Learning, Classification

Abstract

Convolutional neural network (CNN) based image classifiers always take input as an image, automatically learn its feature and classify into predefined output class. If input image resolution varies, then it hinders classification performance of CNN based image classifier. This paper proposes a methodology (training testing methods TOTV, TVTV) and presents the experimental study on the effects of varying resolution on CNN based image classification for standard image dataset MNIST and CIFAR10. The experimental result shows that degradation in resolution from higher to lower decreases performance score (accuracy, precision and F1 score) of CNN based Image classification.

References

[1] Guo, Yanming, et al. "Deep learning for visual understanding: A review." Neurocomputing, Vol.187, pp.27-48, 2016.

[2] Sheikh, H. R., and A. C. Bovik. "Image information and visual quality.", IEEE Transactions on Image Processing,Vol.15, Issue.2, pp.430-444, 2006.

[3] Lu, Dengsheng, and Qihao Weng. "A survey of image classification methods and techniques for improving classification performance.", International Journal of Remote sensing, Vol.28, Issue.5, pp.823-870, 2007.

[4] Hoo-Chang, Shin, et al. "Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning.", IEEE transactions on medical imaging, Vol.35, Issues.5, pp.1285, 2016.

[5] Deng, Li, and Dong Yu. "Deep learning: methods and applications.", Foundations and Trends® in Signal Processing Vol.7, Issue.3–4, pp.197-387, 2014.

[6] Dodge, Samuel, and Lina Karam. "Understanding how image quality affects deep neural networks.", Quality of Multimedia Experience (QoMEX), 2016 Eighth International Conference on. IEEE, 2016.

[7] Basu, Saikat, et al. "Learning sparse feature representations using probabilistic quadtrees and deep belief nets.", Neural Processing Letters, Vol.45, Issue.3, pp.855-867, 2017.

[8] Dejean-Servières, Mathieu, et al. “Study of the Impact of Standard Image Compression Techniques on Performance of Image Classification with a Convolutional Neural Network”, Diss. INSA Rennes; Univ Rennes; IETR; Institut Pascal, 2017.

[9] Sanchez, Angel, et al. "Analyzing the influence of contrast in large-scale recognition of natural images.", Integrated Computer-Aided Engineering, Vol.23, Issue.3, pp.221-235, 2016.

[10] Chevalier, Marion, et al. "LR-CNN for fine-grained classification with varying resolution.", Image Processing (ICIP), 2015 IEEE International Conference on. IEEE, 2015.

[11] Ullman, Shimon, et al. "Atoms of recognition in human and computer vision.", Proceedings of the National Academy of Sciences, Vol.113, Issue.10, pp.2744-2749, 2016.

[12] Chen*, D., D. A. Stow, and P. Gong. "Examining the effect of spatial resolution and texture window size on classification accuracy: an urban environment case.", International Journal of Remote Sensing, Vol.25, Issue.11, pp.2177-2192, 2004.

[13] Sokolova, Marina, and Guy Lapalme. "A systematic analysis of performance measures for classification tasks.", Information Processing & Management, Vol.45, Issue.4,pp. 427-437,2009.

[14] Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner. "Gradient-based learning applied to document recognition.", Proceedings of the IEEE, Vol.86, Issue.11, pp.2278-2324, 1998.

[15] Krizhevsky, Alex, and Geoffrey Hinton., “Learning multiple layers of features from tiny images”, Technical report, University of Toronto, Vol.1, Issue.4, 2009.

Downloads

Published

2018-09-30
CITATION
DOI: 10.26438/ijcse/v6i9.451456
Published: 2018-09-30

How to Cite

[1]
S. P. Kannojia and G. Jaiswal, “Effects of Varying Resolution on Performance of CNN based Image Classification: An Experimental Study”, Int. J. Comp. Sci. Eng., vol. 6, no. 9, pp. 451–456, Sep. 2018.

Issue

Vol. 6 No. 9 (2018): IJCSE September Editor

Section

Research Article

License

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Authors contributing to this journal agree to publish their articles under the Creative Commons Attribution 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 and that in the event of reuse or distribution, the terms of this license are made clear.