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

作者：Zhang Xi Jin; Lu Yi Fan; Zhang Song Hai^*

来源：Journal of Computer Science and Technology, 2016, 31(3): 489-500.

DOI：10.1007/s11390-016-1642-6

• multi-task learning
• convolutional neural network
• food recognition
• machine learning

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.

• 出版日期2016-5
• 单位清华大学