Fish Segmentation And Classification For Large Scale Dataset From Turkey
Classification helps humans learn about different kinds of fish, their features, similarities, and differences. In this project, images from eight fish types are collected from a supermarket’s fish counter; every kind of fish has 1000 images. This study aims to extract fish’s texture, color, and sha...
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| Format: | Undergraduates Project Papers |
| Language: | English |
| Published: |
2022
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| Online Access: | http://umpir.ump.edu.my/id/eprint/39878/ http://umpir.ump.edu.my/id/eprint/39878/1/EA18024_Nur%20Amirah%20Shafiqah%20Salleh_Thesis%20-%20NUR%20AMIRAH%20SHAFIQAH%20SALLEH.pdf |
| _version_ | 1848825890459353088 |
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| author | Nur Amirah Shafiqah, Salleh |
| author_facet | Nur Amirah Shafiqah, Salleh |
| author_sort | Nur Amirah Shafiqah, Salleh |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | Classification helps humans learn about different kinds of fish, their features, similarities, and differences. In this project, images from eight fish types are collected from a supermarket’s fish counter; every kind of fish has 1000 images. This study aims to extract fish’s texture, color, and shape and utilize K-Nearest Neighbors (KNN) and Support Vector Machine (SVM) classifiers to categorize the eight different types of fish in Izmir, Turkey. The results from the experiment show the accuracy of KNN is 100% and SVM is 100%. |
| first_indexed | 2025-11-15T03:36:07Z |
| format | Undergraduates Project Papers |
| id | ump-39878 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T03:36:07Z |
| publishDate | 2022 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-398782024-01-05T08:23:26Z http://umpir.ump.edu.my/id/eprint/39878/ Fish Segmentation And Classification For Large Scale Dataset From Turkey Nur Amirah Shafiqah, Salleh TA Engineering (General). Civil engineering (General) TK Electrical engineering. Electronics Nuclear engineering Classification helps humans learn about different kinds of fish, their features, similarities, and differences. In this project, images from eight fish types are collected from a supermarket’s fish counter; every kind of fish has 1000 images. This study aims to extract fish’s texture, color, and shape and utilize K-Nearest Neighbors (KNN) and Support Vector Machine (SVM) classifiers to categorize the eight different types of fish in Izmir, Turkey. The results from the experiment show the accuracy of KNN is 100% and SVM is 100%. 2022-06 Undergraduates Project Papers NonPeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/39878/1/EA18024_Nur%20Amirah%20Shafiqah%20Salleh_Thesis%20-%20NUR%20AMIRAH%20SHAFIQAH%20SALLEH.pdf Nur Amirah Shafiqah, Salleh (2022) Fish Segmentation And Classification For Large Scale Dataset From Turkey. College of Engineering, Universiti Malaysia Pahang Al-Sultan Abdullah. |
| spellingShingle | TA Engineering (General). Civil engineering (General) TK Electrical engineering. Electronics Nuclear engineering Nur Amirah Shafiqah, Salleh Fish Segmentation And Classification For Large Scale Dataset From Turkey |
| title | Fish Segmentation And Classification For Large Scale Dataset From Turkey |
| title_full | Fish Segmentation And Classification For Large Scale Dataset From Turkey |
| title_fullStr | Fish Segmentation And Classification For Large Scale Dataset From Turkey |
| title_full_unstemmed | Fish Segmentation And Classification For Large Scale Dataset From Turkey |
| title_short | Fish Segmentation And Classification For Large Scale Dataset From Turkey |
| title_sort | fish segmentation and classification for large scale dataset from turkey |
| topic | TA Engineering (General). Civil engineering (General) TK Electrical engineering. Electronics Nuclear engineering |
| url | http://umpir.ump.edu.my/id/eprint/39878/ http://umpir.ump.edu.my/id/eprint/39878/1/EA18024_Nur%20Amirah%20Shafiqah%20Salleh_Thesis%20-%20NUR%20AMIRAH%20SHAFIQAH%20SALLEH.pdf |