Flower classification using deep convolutional neural networks
Flower classification is a challenging task due to the wide range of flower species, which have a similar shape, appearance or surrounding objects such as leaves and grass. In this study, the authors propose a novel two‐step deep learning classifier to distinguish flowers of a wide range of species....
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2018-09-01
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| Series: | IET Computer Vision |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/iet-cvi.2017.0155 |
| _version_ | 1797684827832451072 |
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| author | Hazem Hiary Heba Saadeh Maha Saadeh Mohammad Yaqub |
| author_facet | Hazem Hiary Heba Saadeh Maha Saadeh Mohammad Yaqub |
| author_sort | Hazem Hiary |
| collection | DOAJ |
| description | Flower classification is a challenging task due to the wide range of flower species, which have a similar shape, appearance or surrounding objects such as leaves and grass. In this study, the authors propose a novel two‐step deep learning classifier to distinguish flowers of a wide range of species. First, the flower region is automatically segmented to allow localisation of the minimum bounding box around it. The proposed flower segmentation approach is modelled as a binary classifier in a fully convolutional network framework. Second, they build a robust convolutional neural network classifier to distinguish the different flower types. They propose novel steps during the training stage to ensure robust, accurate and real‐time classification. They evaluate their method on three well known flower datasets. Their classification results exceed 97% on all datasets, which are better than the state‐of‐the‐art in this domain. |
| first_indexed | 2024-03-12T00:35:22Z |
| format | Article |
| id | doaj.art-a4a294c6896641c6872802c3d899fbd4 |
| institution | Directory Open Access Journal |
| issn | 1751-9632 1751-9640 |
| language | English |
| last_indexed | 2024-03-12T00:35:22Z |
| publishDate | 2018-09-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Computer Vision |
| spelling | doaj.art-a4a294c6896641c6872802c3d899fbd42023-09-15T09:46:18ZengWileyIET Computer Vision1751-96321751-96402018-09-0112685586210.1049/iet-cvi.2017.0155Flower classification using deep convolutional neural networksHazem Hiary0Heba Saadeh1Maha Saadeh2Mohammad Yaqub3Computer Science DepartmentThe University of JordanAmmanJordanComputer Science DepartmentThe University of JordanAmmanJordanComputer Science DepartmentThe University of JordanAmmanJordanDepartment of Engineering ScienceInstitute of Biomedical Engineering, University of OxfordOxfordUKFlower classification is a challenging task due to the wide range of flower species, which have a similar shape, appearance or surrounding objects such as leaves and grass. In this study, the authors propose a novel two‐step deep learning classifier to distinguish flowers of a wide range of species. First, the flower region is automatically segmented to allow localisation of the minimum bounding box around it. The proposed flower segmentation approach is modelled as a binary classifier in a fully convolutional network framework. Second, they build a robust convolutional neural network classifier to distinguish the different flower types. They propose novel steps during the training stage to ensure robust, accurate and real‐time classification. They evaluate their method on three well known flower datasets. Their classification results exceed 97% on all datasets, which are better than the state‐of‐the‐art in this domain.https://doi.org/10.1049/iet-cvi.2017.0155flower classificationdeep convolutional neural networksflower speciestwo-step deep learning classifierrobust convolutional neural network classifiertraining stage |
| spellingShingle | Hazem Hiary Heba Saadeh Maha Saadeh Mohammad Yaqub Flower classification using deep convolutional neural networks IET Computer Vision flower classification deep convolutional neural networks flower species two-step deep learning classifier robust convolutional neural network classifier training stage |
| title | Flower classification using deep convolutional neural networks |
| title_full | Flower classification using deep convolutional neural networks |
| title_fullStr | Flower classification using deep convolutional neural networks |
| title_full_unstemmed | Flower classification using deep convolutional neural networks |
| title_short | Flower classification using deep convolutional neural networks |
| title_sort | flower classification using deep convolutional neural networks |
| topic | flower classification deep convolutional neural networks flower species two-step deep learning classifier robust convolutional neural network classifier training stage |
| url | https://doi.org/10.1049/iet-cvi.2017.0155 |
| work_keys_str_mv | AT hazemhiary flowerclassificationusingdeepconvolutionalneuralnetworks AT hebasaadeh flowerclassificationusingdeepconvolutionalneuralnetworks AT mahasaadeh flowerclassificationusingdeepconvolutionalneuralnetworks AT mohammadyaqub flowerclassificationusingdeepconvolutionalneuralnetworks |