17 March 2017 Classification of foods by transferring knowledge from ImageNet dataset
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Proceedings Volume 10341, Ninth International Conference on Machine Vision (ICMV 2016); 1034128 (2017) https://doi.org/10.1117/12.2268737
Event: Ninth International Conference on Machine Vision, 2016, Nice, France
Abstract
Automatic classification of foods is a way to control food intake and tackle with obesity. However, it is a challenging problem since foods are highly deformable and complex objects. Results on ImageNet dataset have revealed that Convolutional Neural Network has a great expressive power to model natural objects. Nonetheless, it is not trivial to train a ConvNet from scratch for classification of foods. This is due to the fact that ConvNets require large datasets and to our knowledge there is not a large public dataset of food for this purpose. Alternative solution is to transfer knowledge from trained ConvNets to the domain of foods. In this work, we study how transferable are state-of-art ConvNets to the task of food classification. We also propose a method for transferring knowledge from a bigger ConvNet to a smaller ConvNet by keeping its accuracy similar to the bigger ConvNet. Our experiments on UECFood256 datasets show that Googlenet, VGG and residual networks produce comparable results if we start transferring knowledge from appropriate layer. In addition, we show that our method is able to effectively transfer knowledge to the smaller ConvNet using unlabeled samples.
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Elnaz J. Heravi, Hamed H. Aghdam, Domenec Puig, "Classification of foods by transferring knowledge from ImageNet dataset", Proc. SPIE 10341, Ninth International Conference on Machine Vision (ICMV 2016), 1034128 (17 March 2017); doi: 10.1117/12.2268737; https://doi.org/10.1117/12.2268737
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