Semantic food segmentation for health monitoring

Mazhar Hussain; Alessandro Ortis; Riccardo Polosa; Sebastiano Battiato

doi:10.1117/12.2679721

7 June 2023 Semantic food segmentation for health monitoring

Mazhar Hussain, Alessandro Ortis, Riccardo Polosa, Sebastiano Battiato

Proceedings Volume 12701, Fifteenth International Conference on Machine Vision (ICMV 2022); 127010E (2023) https://doi.org/10.1117/12.2679721
Event: Fifteenth International Conference on Machine Vision (ICMV 2022), 2022, Rome, Italy

Abstract

This paper presents semantic food segmentation to detect individual food items in an image. The presented approach has been developed in the context of the FoodRec project, which aims to study and develop an automatic framework to track and monitor the dietary habits of people, during their smoke quitting protocol. The goal of food segmentation is to train a model that can look at the images of food items and infer semantic information to recognize individual food items present in an image. In this contribution, we propose a novel Convolutional Deconvolutional Pyramid Network 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 demonstrated significant improvements on a well-known public benchmark dataset.

Citation Download Citation

Mazhar Hussain, Alessandro Ortis, Riccardo Polosa, and Sebastiano Battiato "Semantic food segmentation for health monitoring", Proc. SPIE 12701, Fifteenth International Conference on Machine Vision (ICMV 2022), 127010E (7 June 2023); https://doi.org/10.1117/12.2679721

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KEYWORDS

Image segmentation

Semantics

Deconvolution

Convolution

Education and training

Image deconvolution

Deep learning

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