Parisa Pouladzadeh

Measuring Calorie and Nutrition From Food Image

As people across the globe are becoming more interested in watching their weight, eating more healthy, and avoiding obesity, a system that can measure calories and nutrition in every day meals can be very useful. In this paper, we propose a food calorie and nutrition measurement system that can help patients and dietitians to measure and manage daily food intake. Our system is built on food image processing and uses nutritional fact tables. Recently, there has been an increase in the usage of personal mobile technology such as smartphones or tablets, which users carry with them practically all the time. Via a special calibration technique, our system uses the built-in camera of such mobile devices and records a photo of the food before and after eating it to measure the consumption of calorie and nutrient components. Our results show that the accuracy of our system is acceptable and it will greatly improve and facilitate current manual calorie measurement techniques.

Cloud-based SVM for food categorization

As people across the globe are becoming more interested in watching their weight, eating more healthily, and avoiding obesity, a system that can measure calories and nutrition in everyday meals can be very useful. Recently, due to ubiquity of mobile devices such as smart phones, the health monitoring applications are accessible by the patients practically all the time. We have created a semi-automatic food calorie and nutrition measurement system via mobile that can help patients and dietitians to measure and manage daily food intake. While segmentation and recognition are the two main steps of a food calorie measurement system, in this paper we have focused on the recognition part and mainly the training phase of the classification algorithm. This paper presents a cloud-based Support Vector Machine (SVM) method for classifying objects in cluster. We propose a method for food recognition application that is referred to as the Cloud SVM training mechanism in a cloud computing environment with Map Reduce technique for distributed machine learning. The results show that by using cloud computing system in classification phase and updating the database periodically, the accuracy of the recognition step has increased in single food portion, non-mixed and mixed plate of food compared to LIBSVM.

A novel method for measuring nutrition intake based on food image

In this paper, a food nutrition and energy intake recognition system for medical purposes is proposed. This system is built based on food image processing and shape recognition in addition to nutritional fact tables. Recently, countless studies suggested that the usage of technology such as smartphones may enhance the treatments for obesity and overweight patients. Via a special technique, the system records a photo of the food before and after eating in order to estimate the consumption calorie of the selected food and its nutrients components. Our system presents a new instrument in food intake measuring systems which can be useful and effective in obesity management.

An image procesing approach for calorie intake measurement

Obesity in the world has spread to epidemic proportions. In 2008 the World Health Organization (WHO) reported that 1.5 billion adults were suffering from some sort of overweightness. Obesity treatment requires constant monitoring and a rigorous control and diet to measure daily calorie intake. These controls are expensive for the health care system, and the patient regularly rejects the treatment because of the excessive control over the user. Recently, studies have suggested that the usage of technology such as smartphones may enhance the treatments of obesity and overweight patients; this will generate a degree of comfort for the patient, while the dietitian can count on a better option to record the food intake for the patient. In this paper we propose a smart system that takes advantage of the technologies available for the Smartphones, to build an application to measure and monitor the daily calorie intake for obese and overweight patients. Via a special technique, the system records a photo of the food before and after eating in order to estimate the consumption calorie of the selected food and its nutrient components. Our system presents a new instrument in food intake measuring which can be more useful and effective.

Using Graph Cut Segmentation for Food Calorie Measurement

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].

A Novel SVM Based Food Recognition Method for Calorie Measurement Applications

Emerging food classification methods play an important role in nowadays food recognition applications. For this purpose, a new recognition algorithm for food is presented, considering its shape, color, size, and texture characteristics. Using various combinations of these features, a better classification will be achieved. Based on our simulation results, the proposed algorithm recognizes food categories with an approval recognition rate of 92.6%, in average.

Mobile cloud based food calorie measurement

Mobile-based applications have become ubiquitous in many aspects of peoples lives over the past few years. Harnessing the potential of this trend for healthcare purposes has become a focal point for researchers and industry, in particular designing applications that can be used by patients as part of their wellness, prevention, or treatment process. Along the way, mobile cloud computing (MCC) has been introduced to be a potential paradigm for mobile health services to overcome the interoperability issues across different information formats. In this paper, we propose a mobile cloud-based food calorie measurement system. Our system provides users with convenient and intelligent mechanisms that allow them to track their food intake and monitor their calorie count. The food recognition technique in our system uses cloud Support Vector Machine (SVM) training mechanism in a cloud computing environment with Map Reduce technique for distributed machine learning. The details of the system and its implementation results are recorded in this paper.

An intelligent cloud-based data processing broker for mobile e-health multimedia applications

Abstract Mobile e-health applications provide users and healthcare practitioners with an insightful way to check users/patients’ status and monitor their daily calorie intake. Mobile e-health applications provide users and healthcare practitioners with an insightful way to check users/patients’ status and monitor their daily activities. This paper proposes a cloud-based mobile e-health calorie system that can classify food objects in the plate and further compute the overall calorie of each food object with high accuracy. The novelty in our system is that we are not only offloading heavy computational functions of the system to the cloud, but also employing an intelligent cloud-broker mechanism to strategically and efficiently utilize cloud instances to provide accurate and improved time response results. The broker system uses a dynamic cloud allocation mechanism that takes decisions on allocating and de-allocating cloud instances in real-time for ensuring the average response time stays within a predefined threshold. In this paper, we further demonstrate various scenarios to explain the workflow of the cloud components including: segmentation, deep learning, indexing food images, decision making algorithms, calorie computation, scheduling management as part of the proposed cloud broker model. The implementation results of our system showed that the proposed cloud broker results in a 45% gain in the overall time taken to process the images in the cloud. With the use of dynamic cloud allocation mechanism, we were able to reduce the average time consumption by 77.21% when 60 images were processed in parallel.

FooDD: Food Detection Dataset for Calorie Measurement Using Food Images

Food detection, classification, and analysis have been the topic of in-depth studies for a variety of applications related to eating habits and dietary assessment. For the specific topic of calorie measurement of food portions with single and mixed food items, the research community needs a dataset of images for testing and training. In this paper we introduce FooDD: a Food Detection Dataset of 3000 images that offer variety of food photos taken from different cameras with different illuminations. We also provide examples of food detection using graph cut segmentation and deep learning algorithms.

Food calorie measurement using deep learning neural network

Accurate methods to measure food and energy intake are crucial for the battle against obesity. Providing users/patients with convenient and intelligent solutions that help them measure their food intake and collect dietary information are the most valuable insights toward long-term prevention and successful treatment programs. In this paper, we propose an assistive calorie measurement system to help patients and doctors succeed in their fight against diet-related health conditions. Our proposed system runs on smartphones, which allow the user to take a picture of the food and measure the amount of calorie intake automatically. In order to identify the food accurately in the system, we use deep convolutional neural networks to classify 10000 high-resolution food images for system training. Our results show that the accuracy of our method for food recognition of single food portions is 99%. The analysis and implementation of the proposed system are also described in this paper.