Multi-Task Learning for Food Identification and Analysis with Deep Convolutional Neural Networks

Xi-Jin Zhang; Yi-Fan Lu; Song-Hai Zhang

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Multi-Task Learning for Food Identification and Analysis with Deep Convolutional Neural Networks

机译：深度卷积神经网络用于食品识别和分析的多任务学习

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摘要

In this paper, we proposed a multi-task system that can identify dish types, food ingredients, and cooking methods from food images with deep convolutional neural networks. We built up a dataset of 360 classes of different foods with at least 500 images for each class. To reduce the noises of the data, which was collected from the Internet, outlier images were detected and eliminated through a one-class SVM trained with deep convolutional features. We simultaneously trained a dish identifier, a cooking method recognizer, and a multi-label ingredient detector. They share a few low-level layers in the deep network architecture. The proposed framework shows higher accuracy than traditional method with handcrafted features, and the cooking method recognizer and ingredient detector can be applied to dishes which are not included in the training dataset to provide reference information for users.

机译：在本文中，我们提出了一种多任务系统，可以识别来自具有深度卷积神经网络的食物图像的菜肴，食品成分和烹饪方法。我们建立了360级不同食物的数据集，每个类别为至少500张图像。为了减少从因特网收集的数据的噪声，通过具有深度卷积特征的单级SVM培训，检测到异常值图像。我们同时培训了盘标识符，烹饪方法识别器和多标签成分检测器。它们在深网络架构中共享一些低级层。所提出的框架比手工特征的传统方法显示出更高的精度，并且烹饪方法识别器和成分检测器可以应用于不包括在训练数据集中的菜肴，以提供用户的参考信息。

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《计算机科学技术学报（英文版）》 |2016年第3期|489-500|共12页
作者
Xi-Jin Zhang; Yi-Fan Lu; Song-Hai Zhang;
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Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China;

Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China;

Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China;

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收录信息中国科学引文数据库(CSCD);中国科技论文与引文数据库(CSTPCD);
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入库时间 2024-01-27 02:51:54

Multi-Task Learning for Food Identification and Analysis with Deep Convolutional Neural Networks

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