Title:
Leveraging Context to Support Automated Food Recognition in Restaurants
Leveraging Context to Support Automated Food Recognition in Restaurants
| dc.contributor.author | Bettadapura, Vinay | |
| dc.contributor.author | Thomaz, Edison | |
| dc.contributor.author | Parnam, Aman | |
| dc.contributor.author | Abowd, Gregory D. | |
| dc.contributor.author | Essa, Irfan | |
| dc.contributor.corporatename | Georgia Institute of Technology. Institute for Robotics and Intelligent Machines | en_US |
| dc.contributor.corporatename | Georgia Institute of Technology. College of Computing | en_US |
| dc.contributor.corporatename | Georgia Institute of Technology. School of Interactive Computing | |
| dc.date.accessioned | 2015-05-29T18:54:15Z | |
| dc.date.available | 2015-05-29T18:54:15Z | |
| dc.date.issued | 2015-01 | |
| dc.description | © 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works. | en_US |
| dc.description | DOI: 10.1109/WACV.2015.83 | |
| dc.description.abstract | The pervasiveness of mobile cameras has resulted in a dramatic increase in food photos, which are pictures re- flecting what people eat. In this paper, we study how tak- ing 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 ad- ditional 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 demon- strate image-based recognition of foods eaten in restaurants by training a classifier with images from restaurant’s on- line menu databases. We evaluate the performance of our system in unconstrained, real-world settings with food im- ages taken in 10 restaurants across 5 different types of food (American, Indian, Italian, Mexican and Thai). | en_US |
| dc.embargo.terms | null | en_US |
| dc.identifier.citation | V. Bettadapura, E. Thomaz, A. Parnami, G. Abowd, and I. Essa (2015), “Leveraging Context to Support Automated Food Recognition in Restaurants,” in Proceedings of IEEE Winter Conference on Applications of Computer Vision (WACV), Jan. 2015, pp. 580-587. | en_US |
| dc.identifier.doi | 10.1109/WACV.2015.83 | |
| dc.identifier.uri | http://hdl.handle.net/1853/53364 | |
| dc.language.iso | en_US | en_US |
| dc.publisher | Georgia Institute of Technology | en_US |
| dc.publisher.original | Institute of Electrical and Electronics Engineers | |
| dc.subject | Classifiers | en_US |
| dc.subject | Food recognition | en_US |
| dc.subject | Image-based recognition | en_US |
| dc.subject | Restaurants | en_US |
| dc.title | Leveraging Context to Support Automated Food Recognition in Restaurants | en_US |
| dc.type | Text | |
| dc.type.genre | Post-print | |
| dc.type.genre | Proceedings | |
| dspace.entity.type | Publication | |
| local.contributor.author | Essa, Irfan | |
| local.contributor.author | Abowd, Gregory D. | |
| local.contributor.corporatename | Institute for Robotics and Intelligent Machines (IRIM) | |
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