Implementation of Classification System for Buddha amulet using GLCM and Wavelet Transform and using Neural Network for Classify
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Abstract
The objective of this paper is to develop a system for extracting amulet information from digital images by using the neural network technique. In classify amulet images from digital cameras in controlled conditions such as distance control, distance between the camera and flowers Light intensity in photography etc. This paper focus on classify amulets of powdered material. The various properties used to extract the original image data are analyzed by the Gray-Level Co-Occurrence Matrices (GLCM) in order to collect the statistical values obtained from the analysis of the floor outline. This paper has tested the system using more than 40 powdered Buddha amulets with a total of more than 1,400 images, with data collected as a prototype of 800 images. The system uses 400 images of the same type and 200 images of Buddha images in different directions. This results in evaluating the efficiency of the system in terms of precision 72.12 %, recall 71.24 % F-measure 73.74 % and accuracy 90.80 %
Keywords : feature extraction, neural network, gray-level co-occurrence matrices, amulet
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