Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

1-2017

Abstract

In social media users like to share food pictures. One intelligent feature, potentially attractive to amateur chefs, is the recommendation of recipe along with food. Having this feature, unfortunately, is still technically challenging. First, the current technology in food recognition can only scale up to few hundreds of categories, which are yet to be practical for recognizing ten of thousands of food categories. Second, even one food category can have variants of recipes that differ in ingredient composition. Finding the best-match recipe requires knowledge of ingredients, which is a fine-grained recognition problem. In this paper, we consider the problem from the viewpoint of cross-modality analysis. Given a large number of image and recipe pairs acquired from the Internet, a joint space is learnt to locally capture the ingredient correspondence from images and recipes. As learning happens at the region level for image and ingredient level for recipe, the model has ability to generalize recognition to unseen food categories. Furthermore, the embedded multi-modal ingredient feature sheds light on the retrieval of best-match recipes. On an in-house dataset, our model can double the retrieval performance of DeViSE, a popular cross-modality model but not considering region information during learning.

Keywords

Cross-modal retrieval, Multi-modality embedding, Recipe retrieval

Discipline

Databases and Information Systems | Graphics and Human Computer Interfaces

Research Areas

Intelligent Systems and Optimization

Publication

MultiMedia Modeling: 23rd International Conference, MMM 2017, Reykjavik, Iceland, January 4-6: Proceedings

Volume

10132

First Page

588

Last Page

600

ISBN

9783319518107

Identifier

10.1007/978-3-319-51811-4_48

Publisher

Springer

City or Country

Cham

Additional URL

https://doi.org/10.1007/978-3-319-51811-4_48

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