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Mask R-CNN and GrabCut Algorithm for an Image-based Calorie Estimation System

Journal of Information Systems Engineering and Business Intelligence · Vol. 8 No. 1 (2022) · pp. 1–10

DOI
10.20473/jisebi.8.1.1-10
ISSN
2443-2555, 2598-6333
Published
2022-04-26
Publisher
Universitas Airlangga
Language
eng
License
creativecommons.org/licenses/by/4.0

Abstract

Background: A calorie estimation system based on food images uses computer vision technology to recognize and count calories. There are two key processes required in the system: detection and segmentation. Many algorithms can undertake both processes, each algorithm with different levels of accuracy. Objective: This study aims to improve the accuracy of calorie calculation and segmentation processes using a combination of Mask R-CNN and GrabCut algorithms. Methods: The segmentation mask generated from Mask R-CNN and GrabCut were combined to create a new mask, then used to calculate the calorie. By considering the image augmentation technique, the accuracy of the calorie calculation and segmentation processes were observed to evaluate the method's performance. Results: The proposed method could achieve a satisfying result, with an average calculation error value of less than 10% and an F1 score above 90% in all scenarios. Conclusion: Compared to earlier studies, the combination of Mask R-CNN and GrabCut could obtain a more satisfying result in calculating food calories with different shapes. Keywords: Augmentation, Calorie Calculation, Detection

Collaboration network of the authors

20 authors · 49 co-authorship links

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  • Politeknik Negeri Bandung
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
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Co-authorOrganisationJoint articles
Priyanto HidayatullahPoliteknik Negeri Bandung2
Muhammad Rizqi SholahuddinAffiliation unknown2
Dimas KurniawanPoliteknik Negeri Bandung1
Wili Akbar NugrahaAffiliation unknown1
Lukmannul Hakim FirdausAffiliation unknown1
Ghessa ThenianaPoliteknik Negeri Bandung1
Sendi SetiawanPoliteknik Negeri Bandung1
Yudi WidhiyasanaPoliteknik Negeri Bandung1
Afif Rana MuhammadPoliteknik Negeri Bandung1
Hamzah Prasetio UtomoPoliteknik Negeri Bandung1
Yayang Setia BudiAffiliation unknown1
Fardan Al JihadAffiliation unknown1
Trisna GelarAffiliation unknown1
Djoko Cahyo Utomo LieharyaniAffiliation unknown1
Mohammad Fathul'ibadAffiliation unknown1
Maolana FirmansyahAffiliation unknown1
Cholid FauziAffiliation unknown1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

Metadata source: OAI-PMH (metadata jurnal) https://e-journal.unair.ac.id/JISEBI/oai, first harvested 29 Okt 2026, last seen 29 Okt 2026.
Original metadata (oai_dc) 
<oai_dc:dc xmlns="http://www.openarchives.org/OAI/2.0/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/  http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
	<dc:title xml:lang="en-US">Mask R-CNN and GrabCut Algorithm for an Image-based Calorie Estimation System</dc:title>
	<dc:creator>Subaran, Tiara Lestari</dc:creator>
	<dc:creator>Semiawan, Transmissia</dc:creator>
	<dc:creator>Syakrani, Nurjannah</dc:creator>
	<dc:description xml:lang="en-US">Background: A calorie estimation system based on food images uses computer vision technology to recognize and count calories. There are two key processes required in the system: detection and segmentation. Many algorithms can undertake both processes, each algorithm with different levels of accuracy.
Objective: This study aims to improve the accuracy of calorie calculation and segmentation processes using a combination of Mask R-CNN and GrabCut algorithms.
Methods: The segmentation mask generated from Mask R-CNN and GrabCut were combined to create a new mask, then used to calculate the calorie. By considering the image augmentation technique, the accuracy of the calorie calculation and segmentation processes were observed to evaluate the method's performance.
Results: The proposed method could achieve a satisfying result, with an average calculation error value of less than 10% and an F1 score above 90% in all scenarios.
Conclusion: Compared to earlier studies, the combination of Mask R-CNN and GrabCut could obtain a more satisfying result in calculating food calories with different shapes.
Keywords: Augmentation, Calorie Calculation, Detection</dc:description>
	<dc:publisher xml:lang="en-US">Universitas Airlangga</dc:publisher>
	<dc:date>2022-04-26</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://e-journal.unair.ac.id/JISEBI/article/view/30060</dc:identifier>
	<dc:identifier>10.20473/jisebi.8.1.1-10</dc:identifier>
	<dc:source xml:lang="en-US">Journal of Information Systems Engineering and Business Intelligence; Vol. 8 No. 1 (2022): April; 1-10</dc:source>
	<dc:source>2443-2555</dc:source>
	<dc:source>2598-6333</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://e-journal.unair.ac.id/JISEBI/article/view/30060/17519</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2022 The Authors. Published by Universitas Airlangga.</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by/4.0</dc:rights>
</oai_dc:dc>