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Anushya, A.
- Assessment of Support Vector Machine for Classification of Sardine Images
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1 Department of Computer Science, St. Jerome’s College, IN
1 Department of Computer Science, St. Jerome’s College, IN
Source
ICTACT Journal on Image and Video Processing, Vol 10, No 3 (2020), Pagination: 2141-2144Abstract
The goal of image classification is to forecast the types of the input image using its features. This research focuses on the classification of sardine fish images via Support Vector Machine. Sardines are abundant in the Pacific and Atlantic Oceans, and are available in all the fish markets around the world. Hence, it is the most common sea food in wide-reaching. The sardine fish has a distinct appearance that sets it apart from other types of fish. So, finding out the best-quality fishes is a task that requires the benefit of classification. The sardine images used for the study are collected from Kanyakumari district, Tamil Nadu, India. Gray-Level Co-Occurrence Matrix (GLCM) is used for the Texture Analysis of the images and to extract the statistical features of images. SVM is applied on data and the categories dates are obtained. Both the algorithms are executed in MATLAB and the experiments are carried out for getting better results.Keywords
Classification, Image Classification, Image Processing, Support Vector Machine.
References
- Alex Krizhevsky, lIya Sutskever and Geoffrey E. Hinton, “ImageNet Classification with Deep Convolutional Neural Networks”, Communications of the ACM, Vol. 60, No. 6, pp. 75-79, 2014.
- C. Pornpanomchai, B. Lurstwut and P. Leerasakultham, “Shape and Texture based Fish Image Recognition System”, Kasetsart Journal, Vol. 47, pp. 624-634, 2013.
- D.B. Sheng, S.B. Kim and N.T. Hai, “Fish Injured Rate Measurement Using Color Image Segmentation Method Based on K-Means Clustering Algorithm and Otsu’s Threshold Algorithm”, Journal of Korean Society for Power System Engineering, Vol. 20, No. 4, pp. 32-37, 2016.
- A.H. Serna and L.F.J. Segura, “Automatic Identification of Species with Neural Networks”, Journal of Life and Environmental Sciences, Vol. 23, No. 1, pp. 1-8, 2014.
- Tensorflow and Deep Learning, Available at: https://github.com/GoogleCloudPlatform/tensorflow-without-a-phd/tree/master/tensorflow-mnist-tutorial.
- Le Hoang Thai, Tran Son Hai and Nguyen Thanh Thuy, “Image Classification using Support Vector Machine and Artificial Neural Networks”, International Journal of Information Technology and Computer Science, Vol. 5, pp. 32-38, 2012.
- M.E. Nilsback, and A. Zisserman, “A Visual Vocabulary for Flower Classification”, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 1447-1454, 2006.
- M.E. Nilsback, and A. Zisserman, “Automated Flower Classification over a Large Number of Classes”, Proceedings of IEEE 6th International Conference on Computer Vision, Graphics and Image Processing, pp. 722-729, 2008.
- N.B.A. Mustafa, K. Arumugam, S.K. Ahmed and Z.A.M. Sharrif, “Classification of Vegetables using Probabilistic Neural Networks-Improvement using Color Features”, Proceedings of IEEE International Conference on Technology, Knowledge and Society, pp. 122-128, 2011.
- Om Patil and Vijay Gaikwand, “Classification of Vegetables using TensorFlow”, International Journal for Research in Applied Science and Engineering Technology, Vol. 6, No. 4, pp. 2926-2934, 2018.
- S.O. Ogunlana, O. Olabode and G.B. Iwasokun, “Fish Image Classification using Support Vector Machine”, African Journal of Computing and ICT, Vol. 8, No. 2, pp. 1-8, 2015.
- D.S. Adebayo and O. Olumide, “Fish Classification Algorithm using Single Value Decomposition”, International Journal of Innovative Research in Science, Vol. 5, Issue 2, pp. 1621-1626, 2016.
- D. Kaeli, P. Mistry, D. Schaa and D.P. Zhang, “Heterogeneous Computing with OpenCL 2.0”, Morgan Kaufmann Publisher, 2015.
- A. Anushya, “Image Clustering using K-Means on Marine Products”, International Journal of Innovative Technology and Exploring Engineering, Vol. 9, No. 4, pp. 1-13, 2020.