Flower image classification modeling using neural network
Image processing plays an important role in extracting useful information from images.However, the image processing and the process of translating an image into a statistical distribution of low-level features is not an easy task.These tasks are complicated since the acquired image data often noisy,...
| Main Authors: | , , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
2014
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| Subjects: | |
| Online Access: | https://repo.uum.edu.my/id/eprint/14116/1/07042605.pdf |
| _version_ | 1803626729244196864 |
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| author | Siraj, Fadzilah Mohd Ekhsan, Hawa Zulkifli, Abdul Nasir |
| author_facet | Siraj, Fadzilah Mohd Ekhsan, Hawa Zulkifli, Abdul Nasir |
| author_sort | Siraj, Fadzilah |
| collection | UUM |
| description | Image processing plays an important role in extracting useful information from images.However, the image processing and the process of translating an image into a statistical distribution of low-level features is not an easy task.These tasks are complicated since the acquired image data often noisy, and target objects are influenced by lighting, intensity or illumination. In the case of flower classification, image processing is a crucial step for computer-aided plant species identification. Flower image classification is based on the low-level features such as colour and texture to define and describe the image content. Colour features are extracted using normalized colour histogram and texture features are extracted using gray-level co-occurrence matrix.In this study, a dataset consists of 180 patterns with 7 attributes for each type of flower has been gathered. The finding from the study reveals that the number of images generated to represent each type of flower influences the classification accuracy. One interesting observation is that duplication of very hard to learn images assist Neural Network to improve its classification accuracy.This is also another area that could lead to better understanding towards the behaviour of images when applied to Neural Network classification. |
| first_indexed | 2024-07-04T05:54:48Z |
| format | Conference or Workshop Item |
| id | uum-14116 |
| institution | Universiti Utara Malaysia |
| language | English |
| last_indexed | 2024-07-04T05:54:48Z |
| publishDate | 2014 |
| record_format | dspace |
| spelling | uum-141162016-05-25T08:32:56Z https://repo.uum.edu.my/id/eprint/14116/ Flower image classification modeling using neural network Siraj, Fadzilah Mohd Ekhsan, Hawa Zulkifli, Abdul Nasir QA75 Electronic computers. Computer science Image processing plays an important role in extracting useful information from images.However, the image processing and the process of translating an image into a statistical distribution of low-level features is not an easy task.These tasks are complicated since the acquired image data often noisy, and target objects are influenced by lighting, intensity or illumination. In the case of flower classification, image processing is a crucial step for computer-aided plant species identification. Flower image classification is based on the low-level features such as colour and texture to define and describe the image content. Colour features are extracted using normalized colour histogram and texture features are extracted using gray-level co-occurrence matrix.In this study, a dataset consists of 180 patterns with 7 attributes for each type of flower has been gathered. The finding from the study reveals that the number of images generated to represent each type of flower influences the classification accuracy. One interesting observation is that duplication of very hard to learn images assist Neural Network to improve its classification accuracy.This is also another area that could lead to better understanding towards the behaviour of images when applied to Neural Network classification. 2014-10-21 Conference or Workshop Item PeerReviewed application/pdf en https://repo.uum.edu.my/id/eprint/14116/1/07042605.pdf Siraj, Fadzilah and Mohd Ekhsan, Hawa and Zulkifli, Abdul Nasir (2014) Flower image classification modeling using neural network. In: International Conference on Computer, Control, Informatics and Its Applications, 21-23 Oct. 2014, Bandung, Indonesia. http://doi.org/10.1109/IC3INA.2014.7042605 doi:10.1109/IC3INA.2014.7042605 doi:10.1109/IC3INA.2014.7042605 |
| spellingShingle | QA75 Electronic computers. Computer science Siraj, Fadzilah Mohd Ekhsan, Hawa Zulkifli, Abdul Nasir Flower image classification modeling using neural network |
| title | Flower image classification modeling using neural network |
| title_full | Flower image classification modeling using neural network |
| title_fullStr | Flower image classification modeling using neural network |
| title_full_unstemmed | Flower image classification modeling using neural network |
| title_short | Flower image classification modeling using neural network |
| title_sort | flower image classification modeling using neural network |
| topic | QA75 Electronic computers. Computer science |
| url | https://repo.uum.edu.my/id/eprint/14116/1/07042605.pdf |
| work_keys_str_mv | AT sirajfadzilah flowerimageclassificationmodelingusingneuralnetwork AT mohdekhsanhawa flowerimageclassificationmodelingusingneuralnetwork AT zulkifliabdulnasir flowerimageclassificationmodelingusingneuralnetwork |