基于GLCM特征提取和人工神经网络(ANN)的龙目岛珍珠分类

Muh Nasirudin Karim, R. A. Pramunendar, M. Soeleman, Purwanto Purwanto, Bahtiar Imran
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引用次数: 0

摘要

本研究采用二阶灰度共生矩阵(GLCM)和人工神经网络(ANN)对珍珠图像进行分类。将GLCM方法与人工神经网络相结合用于珍珠图像分类尚无研究。本研究使用的图像数量为360张,分为三个标签,分别是120张A图像、120张AA图像和120张AAA图像。本研究使用的时代为10、20、30、40、50、60、70和80。epoch 10、epoch 20、epoch 30、epoch 40的测试结果准确率分别为80.00%、90.00%、93.33%和94.44%。相比之下,epoch 50的准确率为95.55%,epoch 60的准确率为96.66%,epoch 70的准确率为96.66%,epoch 80的准确率为95.55%。结合上述方法,可以提高珍珠图像分类的准确性,如分类测试结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Classification of Lombok Pearls using GLCM Feature Extraction and Artificial Neural Networks (ANN)
This study used the second-order Gray Level Co-occurrence Matrix (GLCM) and pearl image classification using the Artificial Neural Network (ANN). No previous research combines the GLCM method with artificial neural networks in pearl image classification. The number of images used in this study is 360 images with three labels, including 120 A images, 120 AA images, and 120 AAA images. The epochs used in this study were 10, 20, 30, 40, 50, 60, 70, and 80. The test results at epoch 10 got 80.00% accuracy, epoch 20 got 90.00% accuracy, epoch 30 got 93.33% accuracy, and epoch 40 got 94.44% accuracy. In comparison, epoch 50 got 95.55% accuracy, epoch 60 got 96.66% accuracy, epoch 70 got 96.66% accuracy, and epoch 80 got 95.55% accuracy. The combination of the proposed methods can produce accuracy in classifying pearl images, such as the classification test results.
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