Comparison of image thresholding and clustering segmentation methods for understanding nutritional content of food images

Y. A. Sari, V. Saputra, Andini Agustina, Yudi Arimba Wani, Yusuf Gladiesnyah Bihanda
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引用次数: 5

Abstract

In a hospital, nutritionists and dietitians have to pay attention to how much food consumed by patients since it can affect the nutritional intake they get if patients leftover their meals. Usually, measuring leftover food is done by using the optical measurement, and it may have different prediction values as well when different observers evaluate leftover food. We propose an automatic estimation of the nutritional content of food by focusing on image segmentation from food images. Two algorithms are proposed: image thresholding and K-means++ clustering using HSV color transformation. The result evaluation function shows that image segmentation providing with two-steps thresholding can achieve better rather than using K-means++, with the number of accuracies is 95% and 53.44%, respectively. It concludes that by utilizing the improved image thresholding method, it is robust to identify the food area images that represent as nutritional content.
图像阈值分割和聚类分割方法在理解食品图像营养成分方面的比较
在医院里,营养学家和营养师必须关注病人吃了多少食物,因为如果病人吃完剩下的饭,就会影响他们的营养摄入。通常,剩饭的测量是通过光学测量来完成的,不同的观察者对剩饭的评价可能会有不同的预测值。本文提出了一种基于图像分割的食品营养成分自动估计方法。提出了两种算法:图像阈值分割和基于HSV颜色变换的k -means++聚类。结果评价函数表明,采用两步阈值分割的图像分割效果优于k -means++,正确率分别为95%和53.44%。结果表明,利用改进的图像阈值分割方法,可以较好地识别出代表营养成分的食物区域图像。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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