MEFood: A Large-Scale Representative Benchmark of Quotidian Foods for the Middle East
<p>Automatic food recognition systems have been receiving increasing attention in the research community with the advancements in inductive learning (e.g., classification in computer vision) due to their applicability in the healthcare and hospitality industry. However, food recognition is cha...
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2023
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| _version_ | 1864513560852299776 |
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| author | Mohammed Yusuf Ansari (16904523) |
| author2 | Marwa Qaraqe (10135172) |
| author2_role | author |
| author_facet | Mohammed Yusuf Ansari (16904523) Marwa Qaraqe (10135172) |
| author_role | author |
| dc.creator.none.fl_str_mv | Mohammed Yusuf Ansari (16904523) Marwa Qaraqe (10135172) |
| dc.date.none.fl_str_mv | 2023-01-05T00:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1109/access.2023.3234519 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/MEFood_A_Large-Scale_Representative_Benchmark_of_Quotidian_Foods_for_the_Middle_East/24056235 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Information and computing sciences Artificial intelligence Computer vision and multimedia computation Data management and data science Machine learning Benchmark testing Feature extraction Neural networks Task analysis Deep learning Computer vision Computational modeling Food recognition Benchmark dataset Middle Eastern cuisine |
| dc.title.none.fl_str_mv | MEFood: A Large-Scale Representative Benchmark of Quotidian Foods for the Middle East |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p>Automatic food recognition systems have been receiving increasing attention in the research community with the advancements in inductive learning (e.g., classification in computer vision) due to their applicability in the healthcare and hospitality industry. However, food recognition is challenging due to its fine-grained nature and its high correlation with culture, geo-location, and language. To make food recognition systems feasible for the Middle Eastern region, we present a large-scale dataset (MEFood) of commonly consumed food items in the Middle East, thereby providing a dataset for current development and establishing a benchmark for future research. We have also thoroughly examined the MEFood dataset highlighting its challenging aspects and its real-world nature. Additionally, we have conducted a thorough experimental study benchmarking the mainstream computer vision and mobile networks on classification, runtime, and resource utilization metrics. Our results highlight that EfficientNet-V2 achieves performance closer to the best-performing individual model on the MEFood dataset while having the least resource utilization and minimal inference times. Finally, we have performed a thorough error analysis study to glean additional insights about the networks and MEFood dataset.</p><h2>Other Information</h2><p>Published in: IEEE Access<br>License: <a href="https://creativecommons.org/licenses/by/4.0/legalcode" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/access.2023.3234519" target="_blank">https://dx.doi.org/10.1109/access.2023.3234519</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_822791d47bacda7da87642c6d1a03e02 |
| identifier_str_mv | 10.1109/access.2023.3234519 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/24056235 |
| publishDate | 2023 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | MEFood: A Large-Scale Representative Benchmark of Quotidian Foods for the Middle EastMohammed Yusuf Ansari (16904523)Marwa Qaraqe (10135172)Information and computing sciencesArtificial intelligenceComputer vision and multimedia computationData management and data scienceMachine learningBenchmark testingFeature extractionNeural networksTask analysisDeep learningComputer visionComputational modelingFood recognitionBenchmark datasetMiddle Eastern cuisine<p>Automatic food recognition systems have been receiving increasing attention in the research community with the advancements in inductive learning (e.g., classification in computer vision) due to their applicability in the healthcare and hospitality industry. However, food recognition is challenging due to its fine-grained nature and its high correlation with culture, geo-location, and language. To make food recognition systems feasible for the Middle Eastern region, we present a large-scale dataset (MEFood) of commonly consumed food items in the Middle East, thereby providing a dataset for current development and establishing a benchmark for future research. We have also thoroughly examined the MEFood dataset highlighting its challenging aspects and its real-world nature. Additionally, we have conducted a thorough experimental study benchmarking the mainstream computer vision and mobile networks on classification, runtime, and resource utilization metrics. Our results highlight that EfficientNet-V2 achieves performance closer to the best-performing individual model on the MEFood dataset while having the least resource utilization and minimal inference times. Finally, we have performed a thorough error analysis study to glean additional insights about the networks and MEFood dataset.</p><h2>Other Information</h2><p>Published in: IEEE Access<br>License: <a href="https://creativecommons.org/licenses/by/4.0/legalcode" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/access.2023.3234519" target="_blank">https://dx.doi.org/10.1109/access.2023.3234519</a></p>2023-01-05T00:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1109/access.2023.3234519https://figshare.com/articles/journal_contribution/MEFood_A_Large-Scale_Representative_Benchmark_of_Quotidian_Foods_for_the_Middle_East/24056235CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/240562352023-01-05T00:00:00Z |
| spellingShingle | MEFood: A Large-Scale Representative Benchmark of Quotidian Foods for the Middle East Mohammed Yusuf Ansari (16904523) Information and computing sciences Artificial intelligence Computer vision and multimedia computation Data management and data science Machine learning Benchmark testing Feature extraction Neural networks Task analysis Deep learning Computer vision Computational modeling Food recognition Benchmark dataset Middle Eastern cuisine |
| status_str | publishedVersion |
| title | MEFood: A Large-Scale Representative Benchmark of Quotidian Foods for the Middle East |
| title_full | MEFood: A Large-Scale Representative Benchmark of Quotidian Foods for the Middle East |
| title_fullStr | MEFood: A Large-Scale Representative Benchmark of Quotidian Foods for the Middle East |
| title_full_unstemmed | MEFood: A Large-Scale Representative Benchmark of Quotidian Foods for the Middle East |
| title_short | MEFood: A Large-Scale Representative Benchmark of Quotidian Foods for the Middle East |
| title_sort | MEFood: A Large-Scale Representative Benchmark of Quotidian Foods for the Middle East |
| topic | Information and computing sciences Artificial intelligence Computer vision and multimedia computation Data management and data science Machine learning Benchmark testing Feature extraction Neural networks Task analysis Deep learning Computer vision Computational modeling Food recognition Benchmark dataset Middle Eastern cuisine |