Flower classification using deep convolutional neural networks
Flower classification is a challenging task due to the wide range of flower species which have similar shape, appearance or surrounding objects such as leaves and grass. In this paper, we propose a novel two-step deep learning classifier to distinguish flowers of a wide range of species. Firstly, th...
| Auteurs principaux: | , , , |
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| Format: | Journal article |
| Publié: |
Institution of Engineering and Technology
2018
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| Résumé: | Flower classification is a challenging task due to the wide range of flower species which have similar shape, appearance or surrounding objects such as leaves and grass. In this paper, we propose a novel two-step deep learning classifier to distinguish flowers of a wide range of species. Firstly, 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. Secondly, we build a robust convolutional neural network classifier to distinguish the different flower types. We propose novel steps during the training stage to ensure robust, accurate and real-time classification. We evaluate our method on three well known flower datasets. Our classification results exceed 97% on all datasets which is better than the state-of-the-art in this domain. |
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