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  1. Proceedings of the 9th Workshop on Multimedia for Cooking and Eating Activities in conjunction with The 2017 International Joint Conference on Artificial Intelligence (CEA2017)
  2. Learning Food Appearance by a Supervision with Recipe Text
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Estimation of the Attractiveness of Food Photography Focusing on Main Ingredients
Concurrence of Word Concepts in Cooking Recipe Search
Approaches to Food/Non-food Image Classification Using Deep Learning in Cookpad
A Comparison of Cooking Recipe Named Entities between Japanese and English
Discovering New Creative Mixtures of Cooking Ingredients
Learning Food Appearance by a Supervision with Recipe Text
Multilingualization of Restaurant Menu by Analogical Description
Prediction of Individual Eating Habits Using Short-Term Food Recording
Calculating Cooking Recipe's Difficulty based on Cooking Activities
Method for Detecting Near-duplicate Recipe Creators Based on Cooking Instructions and Food Images

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Learning Food Appearance by a Supervision with Recipe Text

Content Provider ACM Digital Library
Author Harashima, Jun Fujino, Takumi Hashimoto, Atsushi Iiyama, Masaaki Minoh, Michihiko
Abstract We attempt to train a classifier to identify food items in images captured during food preparation processes. Food changes appearance significantly during the cooking process, and different foods can be mixed together. Thus, manually annotating individual food items during the preparation process is difficult. To train a classifier without manual annotation, we used multimedia recipes with stepwise pairs of instructional text and an image. Such stepwise pairs can be informative for training; however, most images contain objects that are not referenced in text (i.e., missing labels) and vice versa (inaccurate labels). To reduce such mismatches, we propose a method that identifies missing labels by searching label candidates from upstream processes in the given recipe. Then, inconsistent word-appearance pairs are removed from the sample based on differences in the model fitting speed using a convolutional neural network. We conducted an experiment using carrot as the target food. The classifier trained using the proposed method outperformed the one that used the method with naive implementation. The proposed method achieved an average precision of 91% compared with 83.8% for the naive implementation.
Starting Page 39
Ending Page 44
Page Count 6
File Format PDF
ISBN 9781450352673
DOI 10.1145/3106668.3106675
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2017-08-20
Publisher Place New York
Access Restriction Subscribed
Subject Keyword Instructional text Weakly supervised learning Food recognition
Content Type Text
Resource Type Article
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