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Title: DietLens-Eout: Large Scale Restaurant Food Photo Recognition
Authors: Zhipeng Wei
Jingjing Chen 
Zhaoyan Ming 
Chong-Wah Ngo 
Tat-Seng Chua 
Fengfeng Zhou
Keywords: Food recognition
Restaurant food recognition
Issue Date: 10-Jun-2019
Citation: Zhipeng Wei, Jingjing Chen, Zhaoyan Ming, Chong-Wah Ngo, Tat-Seng Chua, Fengfeng Zhou (2019-06-10). DietLens-Eout: Large Scale Restaurant Food Photo Recognition. ICMR 2019 : 399-403. ScholarBank@NUS Repository.
Abstract: Restaurant dishes represent a significant portion of food that people consume in their daily life. While people are becoming healthconscious in their food intake, convenient restaurant food tracking becomes an essential task inwellness and fitness applications. Given the huge number of dishes (food categories) involved, it becomes extremely challenging for traditional food photo classification to be feasible in both algorithm design and training data availability. In this work, we present a demo that runs on restaurant dish images in a city of millions of residents and tens of thousand restaurants. We propose a rank-loss based convolutional neural network to optimize the image features representation. Context information such as GPS location of the recognition request is also used to further improve the performance. Our experimental results are highly promising. We have shown in our demo that the proposed algorithm is near ready to be deployed in real-world applications. © 2019 Association for Computing Machinery.
Source Title: ICMR 2019
ISBN: 9781450367653
DOI: 10.1145/3323873.3326923
Appears in Collections:Staff Publications

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