Intelligent Fusion of Deep Features for Improved Waste Classification
<p dir="ltr">In this article, we address the problem of an image-based automatic classification of waste materials. Given the large number of waste categories and the importance of proper management of waste materials, the problem is known to be critical and of a particular interest....
محفوظ في:
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| مؤلفون آخرون: | , |
| منشور في: |
2020
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إضافة وسم
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| _version_ | 1864513505646870528 |
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| author | Kashif Ahmad (12592762) |
| author2 | Khalil Khan (9333883) Ala Al-Fuqaha (4434340) |
| author2_role | author author |
| author_facet | Kashif Ahmad (12592762) Khalil Khan (9333883) Ala Al-Fuqaha (4434340) |
| author_role | author |
| dc.creator.none.fl_str_mv | Kashif Ahmad (12592762) Khalil Khan (9333883) Ala Al-Fuqaha (4434340) |
| dc.date.none.fl_str_mv | 2020-06-04T09:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1109/access.2020.2995681 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Intelligent_Fusion_of_Deep_Features_for_Improved_Waste_Classification/27003508 |
| 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 Waste management waste classification deep features fusion double fusion particle swarm optimization genetic algorithms IOWA Feature extraction Computer architecture Analytical models Computational modeling Correlation Genetic algorithms |
| dc.title.none.fl_str_mv | Intelligent Fusion of Deep Features for Improved Waste Classification |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">In this article, we address the problem of an image-based automatic classification of waste materials. Given the large number of waste categories and the importance of proper management of waste materials, the problem is known to be critical and of a particular interest. To achieve reliable waste classification capability, we propose a novel approach, that we name double fusion, which optimally combines multiple deep learning models using feature and score-level fusion methods. The double fusion scheme ensures an optimized contribution of the deep models by, firstly, combining their capabilities in an early and late fusion scheme followed by a score-level fusion of the classification results obtained with early and late fusion methods. In total, we employ and compare six different fusion methods including two feature-level fusion schemes, namely (i) Discriminant Correlation Analysis and (ii) simple concatenation of deep features, and four late fusion methods, namely (i) Particle Swarm Optimization, (ii) Genetic modeling of deep features (iii) Induced Ordered Weighted Averaging and (iv) a baseline method where all the deep models are treated equally. Moreover, we also evaluate the performance of the individual deep models, and compare our results against state-of-the-art methods demonstrating a significant improvement of 3.58% over state-of-the-art.</p><h2>Other Information</h2><p dir="ltr">Published in: IEEE Access<br>License: <a href="https://creativecommons.org/licenses/by/4.0/deed.en" 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.2020.2995681" target="_blank">https://dx.doi.org/10.1109/access.2020.2995681</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_9ca867a9eaa4c0e26207e51e63df55d0 |
| identifier_str_mv | 10.1109/access.2020.2995681 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/27003508 |
| publishDate | 2020 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Intelligent Fusion of Deep Features for Improved Waste ClassificationKashif Ahmad (12592762)Khalil Khan (9333883)Ala Al-Fuqaha (4434340)Information and computing sciencesArtificial intelligenceComputer vision and multimedia computationData management and data scienceWaste managementwaste classificationdeep featuresfusiondouble fusionparticle swarm optimization genetic algorithmsIOWAFeature extractionComputer architectureAnalytical modelsComputational modelingCorrelationGenetic algorithms<p dir="ltr">In this article, we address the problem of an image-based automatic classification of waste materials. Given the large number of waste categories and the importance of proper management of waste materials, the problem is known to be critical and of a particular interest. To achieve reliable waste classification capability, we propose a novel approach, that we name double fusion, which optimally combines multiple deep learning models using feature and score-level fusion methods. The double fusion scheme ensures an optimized contribution of the deep models by, firstly, combining their capabilities in an early and late fusion scheme followed by a score-level fusion of the classification results obtained with early and late fusion methods. In total, we employ and compare six different fusion methods including two feature-level fusion schemes, namely (i) Discriminant Correlation Analysis and (ii) simple concatenation of deep features, and four late fusion methods, namely (i) Particle Swarm Optimization, (ii) Genetic modeling of deep features (iii) Induced Ordered Weighted Averaging and (iv) a baseline method where all the deep models are treated equally. Moreover, we also evaluate the performance of the individual deep models, and compare our results against state-of-the-art methods demonstrating a significant improvement of 3.58% over state-of-the-art.</p><h2>Other Information</h2><p dir="ltr">Published in: IEEE Access<br>License: <a href="https://creativecommons.org/licenses/by/4.0/deed.en" 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.2020.2995681" target="_blank">https://dx.doi.org/10.1109/access.2020.2995681</a></p>2020-06-04T09:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1109/access.2020.2995681https://figshare.com/articles/journal_contribution/Intelligent_Fusion_of_Deep_Features_for_Improved_Waste_Classification/27003508CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/270035082020-06-04T09:00:00Z |
| spellingShingle | Intelligent Fusion of Deep Features for Improved Waste Classification Kashif Ahmad (12592762) Information and computing sciences Artificial intelligence Computer vision and multimedia computation Data management and data science Waste management waste classification deep features fusion double fusion particle swarm optimization genetic algorithms IOWA Feature extraction Computer architecture Analytical models Computational modeling Correlation Genetic algorithms |
| status_str | publishedVersion |
| title | Intelligent Fusion of Deep Features for Improved Waste Classification |
| title_full | Intelligent Fusion of Deep Features for Improved Waste Classification |
| title_fullStr | Intelligent Fusion of Deep Features for Improved Waste Classification |
| title_full_unstemmed | Intelligent Fusion of Deep Features for Improved Waste Classification |
| title_short | Intelligent Fusion of Deep Features for Improved Waste Classification |
| title_sort | Intelligent Fusion of Deep Features for Improved Waste Classification |
| topic | Information and computing sciences Artificial intelligence Computer vision and multimedia computation Data management and data science Waste management waste classification deep features fusion double fusion particle swarm optimization genetic algorithms IOWA Feature extraction Computer architecture Analytical models Computational modeling Correlation Genetic algorithms |