Table 2:

Food segmentation methods.

Study	Approach	Performance	Reference
Yang et al., 2010	Semantic Texton Forest calculates the probability for each pixel to belong to one of the food classes.	Output from Semantic Texton Forest is far from a precise parsing of an image	[28]
Matsuda et al., 2012	Combined techniques: whole image, DPM, circle detector and JSEG segmentation	Overall accuracy to 21% (top 1) and 45% (top 5)*	[9]
Kawano and Yanai, 2013	Each food item within user generated bound boxes is segmented by GrabCut algorithm	Performance depending on the size of the bounding boxes	[27]
Pouladzadeh et al., 2014	Graph cut segmentation algorithm to extract food items and user's finger	Overall accuracy of 95%	[12]
Shimoda and Yanai, 2015	CNN model searching for food item based on fragmented reference	Detects correct bounding boxes around food items with mean average precision of 49.9% when compared to ground truth values	[30]
Meyers, 2015	DeepLab model	Classification accuracy increases with conditional random fields	[11]
Zhu et al., 2015	Multiple segmentations generated for an image and selected by a classifier	It outperforms normalized cut	[10]
Ciocca et al., 2016	Combines saturazation, binarization, JSEG segmentation and morphological operations	Achieves better segmentation than using JSEG-only approach	[8]

^*Top 1 and/or Top 5 indicate that the performance of the classification model was evaluated based on the first assigned class with the highest probability and/or the top 5 classes among the prediction for each given food item, respectively