文章基本信息

标题：COMPARISON OF TARGET PROBABILISTIC NEURAL NETWORK (PNN) CLASSIFICATION FOR BEEF AND PORK
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作者：LESTARI HANDAYANI ; JASRIL ; ELVIA BUDIANITA 等
期刊名称：Journal of Theoretical and Applied Information Technology
印刷版ISSN：1992-8645
电子版ISSN：1817-3195
出版年度：2017
卷号：95
期号：12
页码：2753
出版社：Journal of Theoretical and Applied
摘要：This research focuses on image recognition of beef and pork. Beef as an example of halal food, while pork as haram food, especially for Muslims. This study used PNN classification and feature extraction methods. These images show some fundamental differences between pork and beef which based on colors and texture. Color was extracted by HSV model, otherwise texture extracted with 3 methods. These methods were Gabor, Principle Component Analysis (PCA) and Local Binary Pattern (LBP). Performance comparison of these methods was measured from the target accuracy of classification. Experiments conducted on 100 images of beef, pork and mixed, with attention to smoothing parameter (spread value/) in PNN and distribution data training and data testing. The best spread value obtained 10 for Gabor+HSV+PNN and LBP+HSV+PNN, but PCA+HSV+PNN was 108. The mixed meat was recognizable by PCA+HSV+PNN and LBP+HSV+PNN equal to 100%. The highest classification performance was achieved by PCA+HSV+PNN. This method can be used to distinguish between meat of permitted food and prohibited food. Mixing pork with beef would be prohibited food for Muslims and other peoples.
关键词：Image Recognition; Local Binary Pattern (LBP); Principle Component Analysis (PCA).