Abstract
Many works have been proposed for food image analysis, such as food recognition and ingredient recognition, in order to facilitate healthcare applications. However, relatively fewer studies have been done on jointly considering multiple factors. In this paper, we think that a food image is better described by not only what food it is but also how it was cooked. We propose neural networks to jointly consider food recognition, ingredient recognition, and cooking method recognition, and verify that recognition performance can be improved by taking multiple factors into account. We collect a food image dataset consisting of clean ingredient information, and demonstrate effectiveness of the proposed recognition models from various viewpoints.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
