Attention Cropping: A Novel Data Augmentation Method for Real-world Plant Species Identification
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This paper investigates the issue of realistic plant species identification. The recognition is close to the condition of a real-world scenario and the dataset is at a large-scale level. A novel data augmentation method is proposed. The image is cropped in terms with visual attention. Different from general way, the cropping is implemented before the image is resized and fed to convolutional neural networks in our proposed method. To deal with the challenge of distinguishing target from complicated background, a method of multiple saliency detections is introduced. Extensive experiments are conducted on both traditional and specific datasets for real-world identification. We introduce the concept of complexity of image background to describe the background complicated rate. Experiments demonstrate that multiple saliency detections can generate corresponding coordinates of the interesting regions well and attention cropping is an efficient data augmentation method. Results show that our method can provide superior performance on different types of datasets. Compared with the precision of methods without attention cropping, the results with attention cropping data augmentation achieve substantial improvement.
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