Smartphone-based Calorie Estimation From Food Image Using Distance Information

Personal assistive systems for diet control can play a vital role to combat obesity. As smartphones have become inseparable companions for a large number of people around the world, designing smartphone-based system is perhaps the best choice at the moment. Using this system people can take an image of their food right before eating, know the calorie content based on the food items on the plate. In this paper, we propose a simple method that ensures both user flexibility and high accuracy at the same time. The proposed system employs capturing food images with a fixed posture and estimating the volume of the food using simple geometry. The real world experiments on different food items chosen arbitrarily show that the proposed system can work well for both regular and liquid food items

Ontology-Driven Food Category Classification in Images

The self-management of chronic diseases related to dietary habits includes the necessity of tracking what people eat. Most of the approaches proposed in the literature classify food pictures by labels describing the whole recipe. The main drawback of this kind of strategy is that a wrong prediction of the recipe leads to a wrong prediction of any ingredient of such a recipe. In this paper we present a multi-label food classification approach, exploiting deep neural networks, where each food picture is classified with labels describing the food categories of the ingredients in each recipe. The aim of our approach is to support the detection of food categories in order to detect which one might be dangerous for a user affected by chronic disease. Our approach relies on background knowledge where recipes, food categories, and their relatedness with chronic diseases are modeled within a state-of-the-art ontology. Experiments conducted on a new publicly released dataset demonstrated the effectiveness of the proposed approach with respect to state-of-the-art classification strategies

Foodopedia: A Convolutional Neural Network Based Food Calorie Estimation

The number of calories consumed determines how healthy a body is in the modern world; therefore, it's important to watch your calorie intake to be healthy. People must keep track of their caloric intake in order to become in shape or maintain a healthy weight. The suggested model uses a deep learning algorithm to offer a novel method of calorie measurement. In the medical area, the estimation of dietary calories is crucial. This measurement is derived from the representation of food in various objects, such as fruits and vegetables. The neural network is used to take this measurement. This technique uses a convolutional neural network to determine the calories in food. An image of food is used as the input for this calculated model. The suggested CNN model uses food object identification to calculate the calorie content of the food. Volume error estimation serves as the primary parameter for the outcome, while calorie error estimation serves as the secondary parameter

深層学習による弱教師あり領域分割とその食事画像への応用

電気通信大学201

GourmetNet: Food Segmentation Using Multi-Scale Waterfall Features With Spatial and Channel Attention

Deep learning and Computer vision are extensively used to solve problems in wide range of domains from automotive and manufacturing to healthcare and surveillance. Research in deep learning for food images is mainly limited to food identification and detection. Food segmentation is an important problem as the first step for nutrition monitoring, food volume and calorie estimation. This research is intended to expand the horizons of deep learning and semantic segmentation by proposing a novel single-pass, end-to-end trainable network for food segmentation. Our novel architecture incorporates both channel attention and spatial attention information in an expanded multi-scale feature representation using the WASPv2 module. The refined features will be processed with the advanced multi-scale waterfall module that combines the benefits of cascade filtering and pyramid representations without requiring a separate decoder or postprocessing

Deep Cooking: Predicting Relative Food Ingredient Amounts from Images

In this paper, we study the novel problem of not only predicting ingredients from a food image, but also predicting the relative amounts of the detected ingredients. We propose two prediction-based models using deep learning that output sparse and dense predictions, coupled with important semi-automatic multi-database integrative data pre-processing, to solve the problem. Experiments on a dataset of recipes collected from the Internet show the models generate encouraging experimental results