Abstract
One of the most common critical factors directly related to the cause of a chronic disease is unhealthy diet consumption. Building an automatic system for food analysis could enable a better understanding of the nutritional information associated to the food consumed and thus, help taking corrective actions on our diet. The Computer Vision community has focused its efforts on several areas involved in visual food analysis such as: food detection, food recognition, food localization, portion estimation, among others. For food detection, the best results in the state of the art were obtained using Convolutional Neural Networks. However, the results of all different approaches were tested on different datasets and, therefore, are not directly comparable. This article proposes an overview of the last advances on food detection and an optimal model based on the GoogLeNet architecture, Principal Component Analysis, and a Support Vector Machine that outperforms the state of the art on two public food/non-food datasets.
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Acknowledgement
This work was partially funded by TIN2015-66951-C2, SGR 1219, CERCA, ICREA Academia’2014, CONICYT Becas Chile, FPU15/01347 and Grant 20141510 (Marató TV3). The funders had no role in the study design, data collection, analysis, and preparation of the manuscript. We acknowledge Nvidia Corporation for the donation of a Titan X GPU.
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Aguilar, E., Bolaños, M., Radeva, P. (2018). Exploring Food Detection Using CNNs. In: Moreno-Díaz, R., Pichler, F., Quesada-Arencibia, A. (eds) Computer Aided Systems Theory – EUROCAST 2017. EUROCAST 2017. Lecture Notes in Computer Science(), vol 10672. Springer, Cham. https://doi.org/10.1007/978-3-319-74727-9_40
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