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.

[1]  Robert M. Haralick,et al.  Textural Features for Image Classification , 1973, IEEE Trans. Syst. Man Cybern..

[2]  Mingui Sun,et al.  Determination of food portion size by image processing , 2008, 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[3]  Shervin Shirmohammadi,et al.  An image procesing approach for calorie intake measurement , 2012, 2012 IEEE International Symposium on Medical Measurements and Applications Proceedings.

[4]  Gunnar Rätsch,et al.  An introduction to kernel-based learning algorithms , 2001, IEEE Trans. Neural Networks.

[5]  Behnoosh Hariri,et al.  A personal assistive system for nutrient intake monitoring , 2011, Ubi-MUI '11.

[6]  M B E Livingstone,et al.  Issues in dietary intake assessment of children and adolescents , 2004, British Journal of Nutrition.

[7]  Yunjie Wu,et al.  An automatic food recognition algorithm with both shape and texture information , 2009, International Conference on Photonics and Image in Agriculture Engineering.

[8]  Da-Wen Sun,et al.  Pizza sauce spread classification using colour vision and support vector machines , 2005 .

[9]  Shervin Shirmohammadi,et al.  A Novel SVM Based Food Recognition Method for Calorie Measurement Applications , 2012, 2012 IEEE International Conference on Multimedia and Expo Workshops.

[10]  Bernhard Schölkopf,et al.  New Support Vector Algorithms , 2000, Neural Computation.

[11]  Barruquer Moner.  IX. References , 1971 .

[12]  Mingui Sun,et al.  A food portion size measurement system for image-based dietary assessment , 2009, 2009 IEEE 35th Annual Northeast Bioengineering Conference.

[13]  Cellular,et al.  Ganong's review of medical physiology , 2016 .

[14]  Anjan Sarkar,et al.  An identification approach for 2-D autoregressive models in describing textures , 1991, CVGIP Graph. Model. Image Process..

[15]  Claude Bouchard,et al.  Handbook of obesity , 1997 .

[16]  Lora E Burke,et al.  Self-monitoring dietary intake: current and future practices. , 2005, Journal of renal nutrition : the official journal of the Council on Renal Nutrition of the National Kidney Foundation.

[17]  J. Beasley,et al.  The pros and cons of using PDAs for dietary self-monitoring. , 2007, Journal of the American Dietetic Association.

[18]  Anil K. Jain,et al.  Unsupervised texture segmentation using Gabor filters , 1990, 1990 IEEE International Conference on Systems, Man, and Cybernetics Conference Proceedings.

[19]  Wen Wu,et al.  Fast food recognition from videos of eating for calorie estimation , 2009, 2009 IEEE International Conference on Multimedia and Expo.

[20]  S. Shirmohammadi,et al.  A novel method for measuring nutrition intake based on food image , 2012, 2012 IEEE International Instrumentation and Measurement Technology Conference Proceedings.

[21]  Amit Jain,et al.  A multiscale representation including opponent color features for texture recognition , 1998, IEEE Trans. Image Process..

[22]  Jindong Tan,et al.  HealthAware: Tackling obesity with health aware smart phone systems , 2009, 2009 IEEE International Conference on Robotics and Biomimetics (ROBIO).

[23]  Vishwanath.B.C,et al.  Recognition of Fruits in Fruits Salad Based on Color and Texture Features , 2012 .