We evaluated the effectiveness in classifying food images of a deep-learning approach based on the specifications of Google's image recognition architecture Inception. The architecture is a deep convolutional neural network (DCNN) having a depth of 54 layers. In this study, we fine-tuned this architecture for classifying food images from three wellknown food image datasets: ETH Food-101, UEC FOOD 100, and UEC FOOD 256. On these datasets we achieved, respectively, 88:28%, 81:45%, and 76:17% as top-1 accuracy and 96:88%, 97:27%, and 92:58% as top-5 accuracy. To the best of our knowledge, these results significantly improve the best published results obtained on the same datasets, while requiring less computation power, since the number of parameters and the computational complexity are much smaller than the competitors'. Because of this, even if it is still rather large, the deep network based on this architecture appears to be at least closer to the requirements for mobile systems.

Food image recognition using very deep convolutional networks / Hassannejad, Hamid; Matrella, Guido; Ciampolini, Paolo; DE MUNARI, Ilaria; Mordonini, Monica; Cagnoni, Stefano. - (2016), pp. 41-49. (Intervento presentato al convegno 2nd International Workshop on Multimedia Assisted Dietary Management, MADiMa 2016 tenutosi a nld nel 2016) [10.1145/2986035.2986042].

Food image recognition using very deep convolutional networks

HASSANNEJAD, Hamid;MATRELLA, Guido;CIAMPOLINI, Paolo;DE MUNARI, Ilaria;MORDONINI, Monica;CAGNONI, Stefano
2016-01-01

Abstract

We evaluated the effectiveness in classifying food images of a deep-learning approach based on the specifications of Google's image recognition architecture Inception. The architecture is a deep convolutional neural network (DCNN) having a depth of 54 layers. In this study, we fine-tuned this architecture for classifying food images from three wellknown food image datasets: ETH Food-101, UEC FOOD 100, and UEC FOOD 256. On these datasets we achieved, respectively, 88:28%, 81:45%, and 76:17% as top-1 accuracy and 96:88%, 97:27%, and 92:58% as top-5 accuracy. To the best of our knowledge, these results significantly improve the best published results obtained on the same datasets, while requiring less computation power, since the number of parameters and the computational complexity are much smaller than the competitors'. Because of this, even if it is still rather large, the deep network based on this architecture appears to be at least closer to the requirements for mobile systems.
2016
978-1-4503-4520-0
Food image recognition using very deep convolutional networks / Hassannejad, Hamid; Matrella, Guido; Ciampolini, Paolo; DE MUNARI, Ilaria; Mordonini, Monica; Cagnoni, Stefano. - (2016), pp. 41-49. (Intervento presentato al convegno 2nd International Workshop on Multimedia Assisted Dietary Management, MADiMa 2016 tenutosi a nld nel 2016) [10.1145/2986035.2986042].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11381/2820548
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