An Identifying the Age of the Tiger Using Data Mining Techniques
AUTHORS
M. Ramaraj,Research Department of Computer Science, NGM College, Pollachi, India
ABSTRACT
Image mining is an astonishing to data mining concept. To understand the data mining concept prior knowledge is more important to image mining. Image mining deals with the extraction of implicit knowledge, image data relationships or other patterns not explicitly stored in the images. It is the process of analyzing large sets of domain-specific data and subsequently extracting information and knowledge in the form of new relationships, patterns, or clusters for the decision-making process. Tiger become a reserved animal. Conservation of tiger has been a challenging task. This work would add a small account to the herculean task of conserving the species. Several scientific researchers have carried out their research on the tiger reserve conservation. This research work proposes a method to find the age of the tiger, using color as a parameter. Color pixel based image classification and clustering techniques has been used to identify the age of the tiger. This research work mainly focuses on RGB color spaces, which is implemented on the real time tiger images. The objective of the research work is to be done on assessing the age of the tiger using the color pixel based image classification and clustering is the main task of the research work and to optimize the image filtering and enhancement methods that are used to remove the noise and to improve the quality of pixels or images and assessing the processing Time, Retrieval Time, Accuracy and Error Rate by generating the better results is real time tiger image database.
KEYWORDS
Data mining techniques, Clustering, classification, Color spaces, Image enhancement method and Performance Analysis
REFERENCES
[1] Nameirakpam Dhanachandra, Khumanthem Manglem, and Yambem Jina Chanu, “Image segmentation using K-Means clustering algorithm and subtractive clustering algorithm,” Procedia computer science, (IMCIP-2015), pp.764-771, (2015)
[2] Nameirakpam Dhanachandra, and Yambem Jina Chanu, “Image segmentation method using K-means clustering algorithm for color image,” Advanced Research in Electrical and Electronic Engineering, vol.2, no.11, pp.68-72, ISSN:2349-5804, July-September, (2015)
[3] Nameirakpam Dhanachandra and Yambem Jina Chanu, “A survey on image segmentation methods using clustering techniques,” EJERS, European Journal of Engineering Research and Science vol.2, no.1, pp.15-20, January, (2017)
[4] Nameirakpam Dhanachandra and Yambem Jina Chanu, “A new approach of image segmentation method using k-means and kernel based subtractive clustering methods,” International Journal of Applied Engineering Research, vol.12, no.20, pp.10458-10464, (2017)
[5] Nishchal K. Verma1, Payal Gupta, Pooja Agrawal, and Yan Cui, “MRI brain image segmentation for spotting tumors using improved mountain clustering approach,” IEEE, (2009)
[6] Junnian Wang and Dunshun Lin, “A modified mountain clustering algorithm based on Hill Valley function,” Journal of Networks, vol.6, no.6, pp.916-922, June, (2011)
[7] Mrs. Namrata and S. Mandvikar, “Augmented reality using contour analysis in E-Learning,” International Journal of Innovative Research in Science, Engineering and Technology, vol.2, no.5, pp.1591-1595, (2013)
[8] Md. Rakib Hassan, Romana Rahman Ema, and Tajul Islam, “Color image segmentation using automated k-means clustering with RGB and HSV color spaces,” Global Journal of Computer Science and Technology: F Graphics & vision, vol.17, no.2, (2017)
[9] K. A. Abdul Nazeer and M. P. Sebastian, “Improving the accuracy and efficiency of the k-means clustering algorithm,” Proceedings of the World Congress on Engineering, vol.1, July 1 - 3, London, U.K., (2009)
[10] Oka Sudana, Darma Putra, Made Sudarma, Rukmi Sari Hartati, and Ayu Wirdiani, “Image clustering of complex balinese character with DBSCAN algorithm,” Journal of Engineering Technology, vol.6, no.1, pp.548-558, January, (2018)
[11] Sandhya G, D Vasumathi, and G T Raju, “A novel DBSCAN approach to identify microcalcifications in cancer images with noise,” International Journal of Computer Applications Technology and Research, vol.3, no.4, pp.245-249, (2014)
[12] S.M. Aqil Burney and Humera Tariq, “K-means cluster analysis for image segmentation,” International Journal of Computer Applications, vol.96, no.4, June, (2014)
[13] Loay. K. Abood and Raad. A. Mohammed, “Image segmentation using superpixel based split and merge method,” Iraqi Journal of Science, vol.56, no.1A, pp.233-237, (2015)
[14] KANG Yonghui, DAI Jiguang, and WANG Guangzhe, “Adaptive fuzzy ISODATA algorithm for color remote sensing image segmentation,” Computer Engineering and Applications, vol.52, no.17, pp.198-202, (2016)
[15] M. Merzougui, M. Nasri, and B. Bouali, “Image segmentation using isodata clustering with parameters estimated by evolutionary approach: Application to quality control,” International Journal of Computer Applications, vol.66, no.19, pp.25-30, (2013)
CITATION
COPYRIGHT
© 2020 M. Ramaraj. Published by Global Vision Press. This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International License (CCBY4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.