Food image recognition with deep convolutional features
In this paper, we report the feature obtained from the Deep Convolutional Neural Network boosts food recognition accuracy greatly by integrating it with conventional hand crafted image features, Fisher Vectors with HoG and Color patches. In the experiments, we have achieved 72.26% as the top 1 accuracy and 92.00% as the top 5 accuracy for the 100 class food dataset, UEC FOOD100, which outperforms the best classification accuracy of this dataset reported so far, 59.6% greatly.
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