Texture-Based Leaf Identification

Abstract

A novel approach to visual leaf identification is proposed. A leaf is represented by a pair of local feature histograms, one computed from the leaf interior, the other from the border. The histogrammed local features are an improved version of a recently proposed rotation and scale invariant descriptor based on local binary patterns (LBPs). Describing the leaf with multi-scale histograms of rotationally invariant features derived from sign- and magnitude-LBP provides a desirable level of invariance. The representation does not use colour. Using the same parameter settings in all experiments and standard evaluation protocols, the method outperforms the state-of-the-art on all tested leaf sets - the Austrian Federal Forests dataset, the Flavia dataset, the Foliage dataset, the Swedish dataset and the Middle European Woods dataset - achieving excellent recognition rates above 99%. Preliminary results on images from the north and south regions of France obtained from the LifeCLEF’14 Plant task dataset indicate that the proposed method is also applicable to recognizing the environmental conditions the plant has been exposed to.