scholarly journals Food Image Recognition via Superpixel Based Low-Level and Mid-Level Distance Coding for Smart Home Applications

2017 ◽  
Vol 9 (5) ◽  
pp. 856 ◽  
Author(s):  
◽  
◽  
Keyword(s):  
Image Recognition ◽  
Smart Home ◽  
Low Level ◽  
Distance Coding ◽  
Food Image

Mid‐level deep Food Part mining for food image recognition

2018 ◽  
Vol 12 (3) ◽  
pp. 298-304 ◽  
Author(s):  
Jiannan Zheng ◽  
Liang Zou ◽  
Z. Jane Wang
Keyword(s):  
Image Recognition ◽  
Food Image

scholarly journals Use of Different Food Image Recognition Platforms in Dietary Assessment: Comparison Study (Preprint)

2019 ◽  
Author(s):  
Stephanie Van Asbroeck ◽  
Christophe Matthys
Keyword(s):  
Image Recognition ◽  
Dietary Assessment ◽  
Application Programming Interface ◽  
Accurate Estimation ◽  
Performance Study ◽  
Comparative Performance ◽  
Dietary Assessment Methods ◽  
Food Quantity ◽  
Application Programming ◽  
Food Image

BACKGROUND In the domain of dietary assessment, there has been an increasing amount of criticism of memory-based techniques such as food frequency questionnaires or 24 hour recalls. One alternative is logging pictures of consumed food followed by an automatic image recognition analysis that provides information on type and amount of food in the picture. However, it is currently unknown how well commercial image recognition platforms perform and whether they could indeed be used for dietary assessment. OBJECTIVE This is a comparative performance study of commercial image recognition platforms. METHODS A variety of foods and beverages were photographed in a range of standardized settings. All pictures (n=185) were uploaded to selected recognition platforms (n=7), and estimates were saved. Accuracy was determined along with totality of the estimate in the case of multiple component dishes. RESULTS Top 1 accuracies ranged from 63% for the application programming interface (API) of the Calorie Mama app to 9% for the Google Vision API. None of the platforms were capable of estimating the amount of food. These results demonstrate that certain platforms perform poorly while others perform decently. CONCLUSIONS Important obstacles to the accurate estimation of food quantity need to be overcome before these commercial platforms can be used as a real alternative for traditional dietary assessment methods.

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