Please use this identifier to cite or link to this item: https://doi.org/10.1145/3123266.3123428
Title: Cross-modal Recipe Retrieval with Rich Food Attributes
Authors: Jingjing Chen
Chong-Wah Ngo
Tat-Seng Chua 
Keywords: Cooking and cutting recognition
Cross-modal retrieval
Ingredient recognition
Recipe retrieval
Issue Date: 23-Oct-2017
Publisher: Association for Computing Machinery, Inc
Citation: Jingjing Chen, Chong-Wah Ngo, Tat-Seng Chua (2017-10-23). Cross-modal Recipe Retrieval with Rich Food Attributes. ACM Multimedia Conference 2017 : 1771-1779. ScholarBank@NUS Repository. https://doi.org/10.1145/3123266.3123428
Abstract: Food is rich of visible (e.g., colour, shape) and procedural (e. cutting, cooking) attributes. Proper leveraging of these attribut particularly the interplay among ingredients, cutting and cooki methods, for health-related applications has not been previous explored. This paper investigates cross-modal retrieval of recip specifically to retrieve a text-based recipe given a food picture query. As similar ingredient composition can end up with wild different dishes depending on the cooking and cutting procedur the difficulty of retrieval originates from fine-grained recogniti of rich attributes from pictures. With a multi-task deep learni model, this paper provides insights on the feasibility of predicti ingredient, cutting and cooking attributes for food recognition a recipe retrieval. In addition, localization of ingredient regions also possible even when region-level training examples are n provided. Experiment results validate the merit of rich attribut when comparing to the recently proposed ingredient-only retriev techniques. © 2017 ACM.
Source Title: ACM Multimedia Conference 2017
URI: https://scholarbank.nus.edu.sg/handle/10635/167453
ISBN: 9781450349062
DOI: 10.1145/3123266.3123428
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