Manuscript received March 22, 2023; revised April 24, 2023; accepted August 17, 2023.
Abstract—This paper presents semantic food segmentation to detect individual food items in an image in the context of Food Recognition (FoodRec) project. FoodRec aims to study and develop an automatic framework to track and monitor the dietary habits of people, during their smoke quitting protocol. Studies have shown a strong correlation between dietary habits’ changes of individuals and smoking cessation process. Abstinence from smoking is associated with several negative effects such as gain of weight, eating disorders, mood changes, and irritability during the initial period of smoke quitting. In this contribution, a novel Convolutional Deconvolutional Pyramid Network (CDPN) is proposed for food segmentation to understand the semantic information of an image at a pixel level. This network employs convolution and deconvolution layers to build a feature pyramid and achieves high-level semantic feature map representation. As a consequence, the novel semantic segmentation network generates a dense and precise segmentation map of the input food image. Furthermore, the proposed method achieved competitive results with 91.77% mean Intersection over Union (IOU) on TrayDataset and 77% mean IOU on MyFood dataset when compared to the state-of-the-art techniques.
Index Terms—Artificial intelligence for health, dietary monitoring, food segmentation, food dataset
Mazhar Hussain is with the Department of Mathematics and Computer Science, University of Catania, Catania, Italy.
Alessandro Ortis and Sebastiano Battiato are with the Department of Mathematics and Computer Science and the Center of Excellence for the Acceleration of HArm Reduction (CoEHAR), University of Catania, Catania, Italy. E-mail: ortis@unict.it (A.O.), battiato@unict.it (S.B.)
Riccardo Polosa is with the Department of Clinical and Experimental Medicine and the Center of Excellence for the Acceleration of HArm Reduction (CoEHAR), University of Catania, Catania, Italy, and ECLAT Srl, Spin-off of the University of Catania, Catania, Italy. E-mail: polosa@unict.it (R.P.)
*Correspondence: mazhar.hussain@phd.unict.it (M.H.)
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Cite:Mazhar Hussain, Alessandro Ortis, Riccardo Polosa, and Sebastiano Battiato, "Semantic Food Segmentation Using Convolutional Deconvolutional Pyramid Network for Health Monitoring," International Journal of Computer Theory and Engineering vol. 15, no. 4, pp. 186-194, 2023.
Copyright © 2023 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
