Journal
IET COMPUTER VISION
Volume 12, Issue 6, Pages 855-862Publisher
WILEY
DOI: 10.1049/iet-cvi.2017.0155
Keywords
biology computing; botany; feedforward neural nets; learning (artificial intelligence); pattern classification; object recognition; flower classification; deep convolutional neural networks; flower species; two-step deep learning classifier; robust convolutional neural network classifier; training stage
Funding
- Innovate UK [101684]
- UK Engineering and Physical Sciences Research Council [EP/L505316/1]
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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.
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