Flower classification using deep convolutional neural networks

Journal article

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.

Authors: Hiary, H; Saadeh, H; Saadeh, M; Yaqub, M

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Institution of Engineering and Technology
• Journal: IET Computer Vision
• Volume: 12
• Issue: 6
• Pages: 855 – 862
• Publication date: 2018-04-11
• Acceptance date: 2018-04-10
• DOI: 10.1049/iet-cvi.2017.0155
• ISSN: 1751-9632 and 1751-9640