Classification of Child Items in a Gold Tree using Support Vector Machine Classifier
Sabeenian.R.S1, Paramasivam.M.E2, Anand.R3, Hariharan.S4
1Dr.R.S.Sabeenian is currently working as a Professor and Head in ECE Department in Sona College of Technology, Salem, Tamil Nadu, India.
2Dr. M.E. Paramasivam is an Assistant professor at Sona College of Technology, Salem, Tamil Nadu. India.
3Anand R, Assistant Professor in Sona College of Technology, Salem, Tamil Nadu, India.
4Mr. Hariharan S, pursuing his master’s degree in Communication System at Sona College of Technology Salem, Tamil Nadu, India.

Manuscript received on November 20, 2019. | Revised Manuscript received on November 26, 2019. | Manuscript published on 30 November, 2019. | PP: 3208-3216 | Volume-8 Issue-4, November 2019. | Retrieval Number: D8026118419/2019©BEIESP | DOI: 10.35940/ijrte.D8026.118419

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Abstract: Sorting of images has been a challenge in Machine Learning Algorithms over the years. Various algorithms have been proposed to sort an image but none of them are able to sort the image clearly. The drawback of the existing systems is that the sorted image is not clearly identified. So, to overcome this drawback we have proposed a novel approach to sort the children of a tree and match them with the existing designs. The images will be sorted on the basis of the class of the image. The images are taken from the image and manual binning of those images are done. Then the images are trained and tested. GLCM feature is extracted from the trained and tested images which are later on fed to the SVM classifier. The classification of image is then done with the help of SVM classifier. Around 7000 images are trained on SVM and used for classification. More than 300 different classes have been created in the database for comparison. Real-time images of child items are captured and fed to the SVM for classifying. The main application of this image is the use in distinguishing the designs in the ornaments. The various parts of the ornaments can be differentiated clearly. Thus, the proposed method is precise as compared to the existing methods.
Keywords: Support Vector Machine, Gray Level Co-occurrence Matrix, Child Item, Tree, Image Classification.
Scope of the Article: Classification.