"snap-n-Eat": Food recognition and nutrition estimation on a smartphone

Journal of Diabetes Science and Technology

Volumn 9, Issue 3, 2015, Pages 525-533

  Zhang, Weiyu ^a   Yu, Qian ^a   Siddiquie, Behjat ^a   Divakaran, Ajay ^a   Sawhney, Harpreet ^a  

^a SRI INTERNATIONAL   (United States)

Author keywords

Food recognition; Mobile food recognition; Nutrition estimation; Visual food recognition

Indexed keywords

CROP; EXTRACT; NUTRITION; PORTION SIZE; RECOGNITION; SMARTPHONE; SUPPORT VECTOR MACHINE; ALGORITHM; CALORIC INTAKE; DIABETES MELLITUS; DIABETIC DIET; DIET THERAPY; FOOD; HUMAN; MOBILE APPLICATION; NUTRITIONAL ASSESSMENT; PERSONALIZED MEDICINE; PHOTOGRAPHY; REPRODUCIBILITY;

ALGORITHMS; DIABETES MELLITUS; DIET, DIABETIC; ENERGY INTAKE; FOOD; HUMANS; MOBILE APPLICATIONS; NUTRITION ASSESSMENT; PHOTOGRAPHY; PRECISION MEDICINE; REPRODUCIBILITY OF RESULTS; SMARTPHONE; SUPPORT VECTOR MACHINE;

EID: 84945189235     PISSN: None     EISSN: 19322968     Source Type: Journal    
DOI: 10.1177/1932296815582222     Document Type: Article

References (8)

1. Puri M, Zhu Z, Yu Q, Divakaran A, Sawhney H. Recognition and volume estimation of food intake using a mobile device. Paper presented at: IEEE Workshop on Applications of Computer Vision; December 2009; Snowbird, UT.
2. Weiss R, Stumbo P, Divakaran A. Automatic food documentation and volume computation using digital imaging and electronic transmission. J Am Diet Assoc. 2010;42:44-110.
3. Yang S, Chen M, Pomerleau D, Sukthankar R. Food recognition using statistics of pairwise local features. Paper presented at: IEEE Conference on Computer Vision and Pattern Recognition; June 2010; San Francisco, CA.
4. Chen MY, Yang YH, Ho CJ, et al. Automatic Chinese food identification and quantity estimation. Paper presented at: SIGGRAPH Asia; November 2012; Singapore.
5. Anthimopoulos M, Dehais J, Diem P, Mougiakakou S. Segmentation and recognition of multi-food meal images for carbohydrate counting. Paper presented at: IEEE 13th International Conference on Bioinformatics and Bioengineering; November 2013; Chania, Greece.
6. Tamrakar A, Ali S, Yu Q, et al. Evaluation of low-level features and their combinations for complex event detection in open source videos. Paper presented at: IEEE Conference on Computer Vision and Pattern Recognition; June 2012; Providence, RI.
7. Lowe DG. Distinctive image features from scale-invariant key-points. Int J Computer Vision. 2004;60:91-110.
8. Perronnin F, Dance C. Fisher kernels on visual vocabularies for image categorization. Paper presented at: IEEE Conference on Computer Vision and Pattern Recognition; June 2007; Minneapolis, MN.