The pervasiveness of mobile cameras has resulted in a dramatic increase in food photos, which are pictures reflecting what people eat. In this paper, we study how taking pictures of what we eat in restaurants can be used for the purpose of automating food journaling. We propose to leverage the context of where the picture was taken, with additional information about the restaurant, available online, coupled with state-of-the-art computer vision techniques to recognize the food being consumed. To this end, we demonstrate image-based recognition of foods eaten in restaurants by training a classifier with images from restaurant's online menu databases. We evaluate the performance of our system in unconstrained, real-world settings with food images taken in 10 restaurants across 5 different types of food (American, Indian, Italian, Mexican and Thai).
With the emergence of low-cost, high-resolution wearable cameras, recording individuals as they perform everyday activities such as eating has been gaining appeal @cite_3 . In this approach, individuals wear cameras that take first-person point-of-view photographs periodically throughout the day. Although first-person point-of-view images offer a viable alternative to direct observation, one of the fundamental problems is image analysis. All captured images must be manually coded for salient content (e.g., evidence of eating activity), a process tends to be tedious and time-consuming.
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Background Objectives: The accuracy of dietary recalls might be enhanced by providing participants with photo images of foods they consumed during the test period. Subjects Methods: We examined the feasibility of a system (Image-Diet Day) that is a user-initiated camera-equipped mobile phone that is programmed to automatically capture and transmit images to a secure website in conjunction with computerassisted, multipass, 24-h dietary recalls in 14 participants during 2007. Participants used the device during eating periods on each of the three independent days. Image processing filters successfully eliminated underexposed, overexposed and blurry images. The captured images were accessed by the participants using the ImageViewer software while completing the 24-h dietary recall on the following day. Results: None of the participants reported difficulty using the ImageViewer. Images were deemed ‘helpful’ or ‘sort of helpful’ by 93 of participants. A majority (79 ) of users reported having no technical problems, but 71 rated the burden of wearing the device as somewhat to very difficult, owing to issues such as limited battery life, self-consciousness about wearing the device in public and concerns about the field of view of the camera. Conclusion: Overall, these findings suggest that automated imaging is a promising technology to facilitate dietary recall. The challenge of managing the thousands of images generated can be met. Smaller devices with a broader field of view may aid in overcoming self-consciousness of the user with using or wearing the device. European Journal of Clinical Nutrition advance online publication, 18 May 2011; doi:10.1038 ejcn.2011.75