Enacting Segmentation Algorithms for Classifying Fish Species
The fundamental feature of Computer vision involves consolidating image processing, pattern recognition and classification procedures. Extricating data from a digital picture relies on upon first distinguishing essential objects or dividing the picture into homogenous sectors or objects termed as segmentation and afterward allotting out these sectors or objects to specific classes termed as classification procedure. The term homogeneous may allude to the shade of the region or an object, however it additionally may utilize different characteristics, for example, composition and shape. This study concentrates on implementing image segmentation and classification on six different fish species using the watershed and the nearest neighbor classifier (kNN) algorithm.
KeywordsImage segmentation Classification Watershed algorithm Nearest neighbor algorithm
- 1.Khan, A. M., & Ravi, S. (2013). Image Segmentation Methods: A Comparative Study.Google Scholar
- 2.Pandey, Madhulika. “An Amalgamated Strategy for Iris Recognition Employing Neural Network and Hamming Distance.” Information Systems Design and Intelligent Applications. Springer India, 2016. 739–747.Google Scholar
- 3.Fu, K. S., & Mui, J. K. (1981). A survey on image segmentation. Pattern recognition, 13(1), 3–16.Google Scholar
- 4.Bhatia, M., Yadav, D., Gupta, P., Kaur, G., Singh, J., Gandhi, M., & Singh, A. (2013, September). Implementing edge detection for medical diagnosis of a bone in Matlab. In Computational Intelligence and Communication Networks (CICN), 2013 5th International Conference on (pp. 270–274). IEEE.Google Scholar
- 5.W.X Kang, R.R Liang. “The comparative research on image segmentation algorithms”, IEEE conference on ETCS, 2009.Google Scholar
- 6.Boiman, O., Shechtman, E., & Irani, M. (2008, June). In defense of nearest-neighbor based image classification. In Computer Vision and Pattern Recognition, 2008. CVPR 2008. IEEE Conference on (pp. 1–8). IEEE.Google Scholar
- 7.Harini, R and C. Chandrashekhar. “Image Segmentation using nearest neighbor classifier on kernel formation”, International conference on pattern recognition Informatics and Medical engineering”, 2012.Google Scholar
- 8.Rafael C, Gonzalez, Richard E Woods. Digital Image Processing (Second Edition). Beijing: Publishing House of Electronics Industry, 2007.Google Scholar
- 9.Yang, Q., & Kang, W. (2009). General research on image segmentation algorithms. International Journal of Image, Graphics and Signal Processing (IJIGSP), 1(1), 1.Google Scholar
- 10.Agarwal, Rashi: Image Processing: [http://www.learningsquare.com].
- 11.Bhatia, M., Bansal, A., Yadav, D., & Gupta, P. (2015). A Proposed Stratification Approach for MRI Images. Indian Journal Of Science And Technology, 8(22). doi: 10.17485/ijst/2015/v8i22/72152.
- 12.Wang, L., Shi, J., Song, G., & Shen, I. F. (2007). Object detection combining recognition and segmentation. In Computer Vision–ACCV 2007 (pp. 189–199). Springer Berlin Heidelberg.Google Scholar
- 13.Tsai, A., Yezzi Jr, A., Wells, W., Tempany, C., Tucker, D., Fan, A.,… & Willsky, A. (2003). A shape-based approach to the segmentation of medical imagery using level sets. Medical Imaging, IEEE Transactions on, 22(2), 137–154.Google Scholar
- 14.[Unattributed]: Fish species: [https://dani20294.wordpress.com/].
- 15.Bhatia, M., Bansal, A., Yadav, D., & Gupta, P. (2015). Proposed Algorithm to Blotch Grey Matter from Tumored and Non Tumored Brain MRI Images. Indian Journal Of Science And Technology, 8(17). doi: 10.17485/ijst/2015/v8i17/63144.
- 16.Bansal, A. (2013). Implementing Edge Detection for Detecting Neurons from Brain to Identify Emotions. International Journal of Computer Applications, 61(9).Google Scholar
- 17.Dr. (Mrs.) G. Padmavathi, Dr. (Mrs.) P. Subashini and Mrs. A. Sumi “Empirical Evaluation of Suitable Segmentation Algorithms for IR Images”, IJCSI International Journal of Computer Science Issues, Vol. 7, Issue 4, No 2, July 2010.Google Scholar
- 18.X. Munoz, J. Freixenet, X. Cuf_ı, J. Mart, “Strategies for image segmentation combining region and boundary information”, Pattern Recognition Letters 24, page no 375–392, 2003.Google Scholar