Dietary-related problems such as obesity are a growing concern in todays modern world. If the current trend continues, it is most likely that the quality of life, in general, is significantly affected since obesity is associated with other chronic diseases such as hypertension, irregular blood sugar levels, and increased risk of heart attacks. The primary cause of these problems is poor lifestyle choices and unhealthy dietary habits, with emphasis on a select few food groups such as sugars, fats, and carbohydrates. In this regard, computer-based food recognition offers automatic visual-based methods to assess dietary intake and help people make healthier choices. Thus, the following paper presents a brief review of visual-based methods for food recognition , including their accuracy, performance, and the use of popular food databases to evaluate existing models. The work further aims to highlight future challenges in this area . New high-quality studies for developing standard benchmarks and using continual learning methods for food recognition are recommended .
Last ten years have witnessed the growth of many computer vision applications for food recognition. Dietary studies showed that dietary-related problem such as obesity is associated with other chronic diseases like hypertension, irregular blood sugar levels, and increased risk of heart attacks. The primary cause of these problems is poor lifestyle choices and unhealthy dietary habits, which are manageable by using interactive mHealth apps that use automatic visual-based methods to assess dietary intake . This review discusses the most performing methodologies that have been developed so far for automatic food recognition. First, we will present the rationale of visual-based methods for food recognition . The core of the paper is the presentation, discussion and evaluation of these methods on popular food image databases. We also discussed the mobile applications that are implementing these methods. The review ends with a discussion of research gaps and future challenges in this area .
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Dietary-related problems
Last ten years have witnessed the growth of many computer vision applications for food recognition. Dietary studies showed that dietary-related problem
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are a growing concern in todays modern world. If the current trend continues, it is most likely that the quality of life, in general, is significantly affected since obesity is
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with emphasis on a select few food groups such as sugars, fats, and carbohydrates. In this regard, computer-based food recognition offers
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and help people make healthier choices. Thus, the following paper presents a brief review
. This review discusses the most performing methodologies that have been developed so far for automatic food recognition. First, we will present the rationale
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, including their accuracy, performance, and the use of popular food databases to evaluate existing models. The work further aims to highlight
. The core of the paper is the presentation, discussion and evaluation of these methods on popular food image databases. We also discussed the mobile applications that are implementing these methods. The review ends with a discussion of research gaps and
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. New high-quality studies for developing standard benchmarks and using continual learning methods for food recognition are recommended