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.

Aslan, S., Ciocca, G., Mazzini, D., Schettini, R. (2020). Benchmarking algorithms for food localization and semantic segmentation. INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS, 11(12), 2827-2847 [10.1007/s13042-020-01153-z].

Benchmarking algorithms for food localization and semantic segmentation

Aslan S.;Ciocca G.;Mazzini D.;Schettini R.
2020

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.
Articolo in rivista - Articolo scientifico
Benchmarking; Convolutional neural network; Food localization; Food segmentation;
English
24-giu-2020
2020
11
12
2827
2847
reserved
Aslan, S., Ciocca, G., Mazzini, D., Schettini, R. (2020). Benchmarking algorithms for food localization and semantic segmentation. INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS, 11(12), 2827-2847 [10.1007/s13042-020-01153-z].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/278479
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