Leaf shape descriptor for tree species identification

The problem of automatic leaf identification is particularly challenging because, in addition to constraints derived from image processing such as geometric deformations (rotation, scale, translation) and illumination variations, it involves difficulties arising from foliar properties. These include two main aspects: the first is the enormous number and diversity of leaf species and the second, which is relevant to some special species, is the high inter-species and the low intra-species similarity. In this paper, we present a novel boundary-based approach that attempts to overcome the most of these constraints. This method has been compared to results obtained in the image CLEF 2011 plant identification task. The main advantage of this first benchmark edition is that different image retrieval techniques were tested and a crowd-sourced leaf dataset was used. Our method provides the best classification rate for scan and scan-like pictures. Besides its high accuracy, our method satisfies real-time requirements with a low computational cost.