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24-06-2020 | Original Article | Issue 12/2020

International Journal of Machine Learning and Cybernetics 12/2020

Benchmarking algorithms for food localization and semantic segmentation

Journal:
International Journal of Machine Learning and Cybernetics > Issue 12/2020
Authors:
Sinem Aslan, Gianluigi Ciocca, Davide Mazzini, Raimondo Schettini
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Abstract

The problem of food segmentation is quite challenging since food is characterized by intrinsic high intra-class variability. Also, segmentation of food images taken in-the-wild may be characterized by acquisition artifacts, and that could be problematic for the segmentation algorithms. A proper evaluating of segmentation algorithms is of paramount importance for the design and improvement of food analysis systems that can work in less-than-ideal real scenarios. In this paper, we evaluate the performance of different deep learning-based segmentation algorithms in the context of food. Due to the lack of large-scale food segmentation datasets, we initially create a new dataset composed of 5000 images of 50 diverse food categories. The images are accurately annotated with pixel-wise annotations. In order to test the algorithms under different conditions, the dataset is augmented with the same images but rendered under different acquisition distortions that comprise illuminant change, JPEG compression, Gaussian noise, and Gaussian blur. The final dataset is composed of 120,000 images. Using standard benchmark measures, we conducted extensive experiments to evaluate ten state-of-the-art segmentation algorithms on two tasks: food localization and semantic food segmentation.

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