Evaluation of Features for Leaf Classification in Challenging Conditions

@article{Hall2015EvaluationOF,
  title={Evaluation of Features for Leaf Classification in Challenging Conditions},
  author={David H Hall and Chris McCool and Feras Dayoub and Niko S{\"u}nderhauf and Ben Upcroft},
  journal={2015 IEEE Winter Conference on Applications of Computer Vision},
  year={2015},
  pages={797-804}
}
Fine-grained leaf classification has concentrated on the use of traditional shape and statistical features to classify ideal images. In this paper we evaluate the effectiveness of traditional hand-crafted features and propose the use of deep convolutional neural network (Conv Net) features. We introduce a range of condition variations to explore the robustness of these features, including: translation, scaling, rotation, shading and occlusion. Evaluations on the Flavia dataset demonstrate that… CONTINUE READING
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