![Open Access](https://i-scholar.in/lib/pkp/templates/images/icons/fulltextgreen.png)
![Restricted Access](https://i-scholar.in/lib/pkp/templates/images/icons/fulltextred.png)
![Open Access](https://i-scholar.in/lib/pkp/templates/images/icons/fulltextgreen.png)
![Open Access](https://i-scholar.in/lib/pkp/templates/images/icons/fulltext_open_medium.gif)
![Restricted Access](https://i-scholar.in/lib/pkp/templates/images/icons/fulltextred.png)
![Restricted Access](https://i-scholar.in/lib/pkp/templates/images/icons/fulltext_restricted_medium.gif)
Selection of an Efficient Image Classifier-A Critical Analysis
Subscribe/Renew Journal
The most important part of image analysis is classification which helps in grouping the pixels in an image into different categories on the basis of their information content. Classification concept which was originated from pattern recognition field has a widespread application in satellite image analysis and medical diagnosis. Classification also helps in reducing the search time by grouping same type of images, so that, the searching operation can be conducted on the specific group of images instead of searching all the images. On the basis of parameters used for classification, the image classification techniques can be divided into different types. A large number of studies have been conducted for classifying images using different types of classification algorithms. The aim of this paper is to perform a detailed review of the classification algorithms and do a comparative study of the work done by the researchers on image classification.
Keywords
- S. A. Medjahed, “A comparative study of feature extraction methods in images classification,” International Journal of Image, Graphics and Signal Processing, Vol 7, No 3, pp. 16-23, 2015.
- D. R. Amancio, C. H. Comin, D. Casanova, G. Travieso, O. M. Bruno, F. A. Rodrigues, L. F. Costa, “A systematic comparison of supervised classifiers,” PLoS ONE, Vol 9, No 4, pp. 1-14, April, 2014.
- D. Lu, Q. Weng, “A survey of image classification methods and techniques for improving classification performance,” International Journal of Remote Sensing, Vol 28, No 5, pp. 823-870, March, 2007.
- S. S. Nath, J. Kar, G. Mishra, S. Chakraborty, “A survey of image classification methods and techniques”, International Conference on Control, Instrumentation, Communication and Computational Technologies , IEEE, pp. 554-557, 2014.
- M. Jain and P. S. Tomar, “Review of image classification methods and techniques”, International Journal of Engineering Research and Technology, Vol 2, No 8, pp. 852-858, August, 2013.
- B. Krishnapuram, D. Williams, Y. Xue, A. Hartemink, L. Carin, M. A. T. Figueiredo, “On semi-supervised classification”, Advances in Neural Information Processing Systems, pp. 721-728, 2005.
- X. Zhu, “Semi-supervised learning literature survey”, 2005.
- Y. Kumar and G. Sahoo, “Analysis of parametric and nonparametric classifiers for classification technique using WEKA”, International Journal of Information Technology and Computer Science, MECS Press, pp. 43-49, July, 2012.
- S. Arunadevi, S. D. M. Raja, “A survey on image classification algorithm based on per-pixel”, International Journal of Engineering Research and General Science, Vol 2, No 6, pp. 387-392, 2014.
- S. Natya and V. J. Rehna, “Land cover classification schemes using remote sensing images: A recent survey”, British Journal of Applied Science and Technology, Vol 13, No 4, pp. 1-11, 2016.
- Y. Liu, H. H. Zhang, Y. Wu, “Hard or soft classification? Large-margin unified machines?” Journal of the American Statistical Association, pp. 166-177, 2011.
- R. Miller, “Hard and soft image classifications”, Geog 581.
- J. Yang, K. Yu, Y. Gong, T. Huang, “Linear spatial pyramid matching using sparse coding for image classification”, IEEE Conference on Computer Vision and Pattern Recognition, pp. 1794-1801, 2009.
- J. Wang, J. Yang, K. Yu, F. Lv, T. Huang, Y. Gong, “Locality-constrained linear coding for image classification”, IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3360-3367, 2010.
- G. M. Foody and A. Mathur, “A relative evaluation of multi-class image classification by support vector machines”, IEEE Transactions on Geoscience and Remote Sensing, Vol 42, No 6, Pp. 1335-1343, 2004.
- A. Bosch, A. Zisserman, X. Muñoz, “Image classification using random forests and ferns”, IEEE 11th International Conference on Computer Vision, pp. 1-8, 2007.
- J. R. Otukei and T. Blaschke, “Land cover change assessment using decision trees, support vector machines and maximum likelihood classification algorithms”, International Journal of Applied Earth Observation and Geoinformation, Pp. S27-S31, 2010.
- C.-C. Yang, S. O. Prasher, P. Enright, C. Madramootoo, M. Burgess, P. K. Goel, I. Callum, “Application of decision tree technology for image classification using remote sensing data”, Agricultural Systems, Elsevier, Pp. 1101-1117, 2003.
- L. Jiang, W. Wang, X. Yang, N. Xie, Y. Cheng, “Classification methods of remote sensing image based on decision tree technologies”, International Conference on Computer and Computing Technologies in Agriculture, Springer, Pp. 353-358, 2010.
- S. McCann and D. G. Lowe, “Local naïve bayes nearest neighbor for image classification”, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Pp. 3650-3656, 2012.
- J. F. Mas and J. J. Flores, “The application of artificial neural networks to the analysis of remotely sensed data”, International Journal of Remote Sensing, Vol 29, No. 3, pp. 617-663, 2008.
- E. Maggiori, Y. Tarabalka, G. Charpiat, P. Alliez, “Convolutional neural networks for large-scale remote sensing image classification”, IEEE Transactions on Geoscience and Remote Sensing, Vol 55, No 2, pp. 645-657, 2017.
- C.-M. Huo, H. Zheng, H.-Y. Su, Z.-L. Sun, Y.-J. Cai, Y.-F. Xu, “Tongue shape classification integrating image processing and convolutional neural network”, 2nd Asia–Pacific Conference on Intelligent Robot Systems, IEEE, Pp. 42-46, 2017.
- R. Kemker and C. Kanan, “Self-taught feature learning for hyperspectral image classification”, IEEE transactions on geoscience and remote sensing, Vol 55, No 5, pp. 2693-2705, May 2017.
- D. Cireşan, U. Meier, J. Schmidhuber, “Multi-column deep neural networks for image classification”, IEEE Conference on Computer Vision and Pattern Recognition, pp. 3642-3649, 2012.
- T. Li, J. Zhang, Y. Zhang, “Classification of hyperspectral image based on deep belief networks”, International Conference on Image Processing (ICIP), IEEE, Pp. 5132-5136, 2014.
- Q. Cheng, P. K. Varshney, M. K. Arora, “Logistic regression for feature selection and soft classification of remote sensing data”, IEEE Geoscience and Remote Sensing Letters, Vol 3, No 4, Pp. 491-494, 2006.
- W. Li, G. Wu, F. Zhang, Q. Du, “Hyperspectral image classification using deep pixel-pair features”, IEEE Transactions on Geoscience and Remote Sensing, Vol 55, No 2, Pp. 844-853, 2017.
- S. B. Kotsiantis, “Supervised machine learning: A review of classification techniques”, Informatica, Vol 31, No 3, pp. 249-268, 2007.
![](https://i-scholar.in/public/site/images/abstractview.png)
Abstract Views: 210
![](https://i-scholar.in/public/site/images/pdfview.png)
PDF Views: 3