Deep cooking: Predicting relative food ingredient amounts from images

Jiatong Li, Ricardo Guerrero, Vladimir Pavlovic

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

In this paper, we study the novel problem of not only predicting ingredients from a food image, but also predicting the relative amounts of the detected ingredients. We propose two prediction-based models using deep learning that output sparse and dense predictions, coupled with important semi-automatic multi-database integrative data pre-processing, to solve the problem. Experiments on a dataset of recipes collected from the Internet show the models generate encouraging experimental results.

Original languageEnglish (US)
Title of host publicationMADiMa 2019 - Proceedings of the 5th International Workshop on Multimedia Assisted Dietary Management, co-located with MM 2019
PublisherAssociation for Computing Machinery, Inc
Pages2-6
Number of pages5
ISBN (Electronic)9781450369169
DOIs
StatePublished - Oct 15 2019
Event5th International Workshop on Multimedia Assisted Dietary Management, MADiMa 2019, held with ACM Multimedia 2019 - Nice, France
Duration: Oct 21 2019 → …

Publication series

NameMADiMa 2019 - Proceedings of the 5th International Workshop on Multimedia Assisted Dietary Management, co-located with MM 2019

Conference

Conference5th International Workshop on Multimedia Assisted Dietary Management, MADiMa 2019, held with ACM Multimedia 2019
CountryFrance
CityNice
Period10/21/19 → …

All Science Journal Classification (ASJC) codes

  • Human-Computer Interaction
  • Computer Graphics and Computer-Aided Design
  • Computer Vision and Pattern Recognition
  • Endocrinology, Diabetes and Metabolism

Keywords

  • Amount estimation
  • Datasets
  • Ingredients
  • Neural networks

Fingerprint Dive into the research topics of 'Deep cooking: Predicting relative food ingredient amounts from images'. Together they form a unique fingerprint.

Cite this