DietLens-eout: Large scale restaurant food photo recognition
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 in wellness and fitness applications. Given the huge number of dishes (food...
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2019
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sg-smu-ink.sis_research-75022022-01-10T04:55:50Z DietLens-eout: Large scale restaurant food photo recognition WEI, Zhipeng CHEN, Jingjing MING, Zhaoyan NGO, Chong-wah CHUA, Tat-Seng ZHOU, Fengfeng 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 in wellness 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. 2019-06-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/6499 info:doi/10.1145/3323873.3326923 https://ink.library.smu.edu.sg/context/sis_research/article/7502/viewcontent/3323873.3326923.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Food recognition Restaurant food recognition Databases and Information Systems Graphics and Human Computer Interfaces |
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Food recognition Restaurant food recognition Databases and Information Systems Graphics and Human Computer Interfaces WEI, Zhipeng CHEN, Jingjing MING, Zhaoyan NGO, Chong-wah CHUA, Tat-Seng ZHOU, Fengfeng DietLens-eout: Large scale restaurant food photo recognition |
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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 in wellness 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. |
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WEI, Zhipeng CHEN, Jingjing MING, Zhaoyan NGO, Chong-wah CHUA, Tat-Seng ZHOU, Fengfeng |
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WEI, Zhipeng CHEN, Jingjing MING, Zhaoyan NGO, Chong-wah CHUA, Tat-Seng ZHOU, Fengfeng |
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WEI, Zhipeng |
title |
DietLens-eout: Large scale restaurant food photo recognition |
title_short |
DietLens-eout: Large scale restaurant food photo recognition |
title_full |
DietLens-eout: Large scale restaurant food photo recognition |
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DietLens-eout: Large scale restaurant food photo recognition |
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DietLens-eout: Large scale restaurant food photo recognition |
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dietlens-eout: large scale restaurant food photo recognition |
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Institutional Knowledge at Singapore Management University |
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2019 |
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https://ink.library.smu.edu.sg/sis_research/6499 https://ink.library.smu.edu.sg/context/sis_research/article/7502/viewcontent/3323873.3326923.pdf |
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