Using graph cut segmentation for food calorie measurement

Abstract

Calorie measurement systems that run on smart phones allow the user to take a picture of the food and measure the number of calories automatically. In order to identify the food accurately in such systems, image segmentation, which partitions an image into different regions, plays an important role. In this paper, we present the implementation of Graph cut segmentation as a means of improving the accuracy of our food classification and recognition system. Graph cut based method is well-known to be efficient, robust, and capable of finding the best contour of objects in an image, suggesting it to be a good method for separating food portions in a food image for calorie measurement. In this paper, we provide the analysis of the Graph cut algorithm as applied to food recognition. We also perform a number of experiments where we used results from the segmentation phase to the Support Vector Machine (SVM) classification model. The results show an improvement in the accuracy of food recognition, especially mixed food where accuracy increases by 15% compared to our previous work [10].

FooDD: Food Detection Dataset for Calorie Measurement Using Food Images

“…In this work, we have combined Graph cut segmentation and deep neural network. The dataset is used in the learning process of these two methods, which allow us to improve the accuracy of our food classification and recognition significant compared to our previous work [1] [3]. By recognizing the food portions and also by having the size and shape of the food portions from graph cut algorithm, we can calculate the calorie of the whole food portions.…”

“…In this section, we briefly explain how our food recognition system works. For a complete description with details about specific image processing and machine learning techniques, design choices, experiments, and the required accuracy, we refer the readers to [1] [2], and [3]. To measure food calorie, we use a mobile device with camera that supports wireless connection, such as any of today's smartphones.…”

“…Acquisition of an accurate dataset will, at the end, support the realization of effective treatment programs for patients. In our previous work [1] [2] [3], we proposed a system using Vision-Based Measurement (VBM) [6] to improve the accuracy of food intake reporting. Our system runs on smartphones and allows the user to take a picture of the food and measure the calorie intake automatically.…”

Cloud based virtualization for a calorie measurement e-health mobile application

“…Comparison of [7] to our model: MapReduce was also used by S. Peddi et al [5] and also used in [6] [7], to achieve the parallel execution of the training and testing task (as part of the image recognition technique) in cloud. The goal by S.Peddi et al [5] was to achieve faster computation for these tasks.…”

A Mobile-Based Diet Monitoring System for Obesity Management

“…With more food logging activities, the system is capable of identifying individuals’ eating patterns and rendering interventions, e.g., recommending healthier food or providing warnings when detecting bad eating habits. To accomplish this, we first explored new methodologies in Computer Vision and Machine Learning to address key issues in each of the following components: 1) a comprehensive food image database that contains diverse and abundant images from a large number of food classes, in order to avoid the food discrepancy when training a food-image classifier [7]; 2) a food segmentation strategy that can correctly identify all items in an image from the background regardless the lighting conditions or if the foods are mixed or not [8]; 3) a Machine Learning model to be trained for classifying each segmented item; 4) volume and weight estimation to be performed on each food item, followed by the nutrient analysis [9,10]. In addition, one unique feature included in this system is a metabolic network simulation that takes into consideration individual’s basal metabolism and monitors the real-time energy production in the presence of nutrients available in the meal.…”