Comparison of Beef Marbling Segmentation by Experts towards Computational Techniques by Using Jaccard, Dice and Cosine

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Hanny Hikmayanti H. , et. al


In Indonesia, the physical quality assessment of beef is determined based on the color of the fat and meat, texture, tenderness and marbling (Badan Standar Nasional, 2018). This research is aimed to compare the beef marbling segmentation conducted by experts towards computational techniques by using Jaccard, Dice and Cosine. The Jaccard similarity index focuses on the proportion of the cardinalities of the images assessed together with the cardinalities of all the images calculated in both segments. Then Cosine similarity calculation is done to calculate marbling similarity by using vector. The Dice Similarity Coefficient is used as a statistical validation metric to evaluate the performance of both manual segmentation reproducibility and the spatial overlapping accuracy of automatic probabilistic fractional segmentation in images. The research began with acquiring beef images using a digital camera. Beef image data used by experts to determine the marbling point is also used in the segmentation process of this study. In the meat image segmentation process, 50 marbling points are determined to get the RGB value of each selected marbling point. Furthermore, the results of the meat image were determined by the ground truth by experts as a knowledge base and computational image segmentation was also carried out. The results of determining ground truth marbling are then tested for similarity with the results of image segmentation. If the results match, the next step was image processing and image segmentation. Furthermore, in the marbling segmentation test, the range of RGB values is then entered as the Beef marbling Tresholding Value by taking the difference in the values of Red and Green as well as Red and Blue. Jaccard, Dice, and Cosine values ranged from 0.3 to 0.6, the results show: Jaccard: 0,397, the Dice: 0,561, Cosine: 0,572. These indicate the similarity of marbling markings performed by experts and computing is small. This is because the expert's sense of sight has limitations in seeing marbling with a small size, while computational techniques are able to calculate marbling to the smallest size. Based on the results described, it can be concluded that assessing marbling with expert vision is not enough. Thus, it needs to be added and validated with computational techniques. In this study, the marbling marking results appear more detailed and accurate. Finally, it can be concluded that computational techniques are able to calculate marbling better than experts

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