Semantic shape models for leaf species identification

Abstract

We present two complementary botanical-inspired leaf shape representation models for the classification of simple leaf species (leaves with one compact blade). The first representation is based on some linear measurements that characterise variations of the overall shape, while the second consists of semantic part-based segment models. These representations have two main advantages: First, they only require the extraction of two points: the base and apex, which are the key characterisation points of simple leaves. The second advantage is the complementary of the proposed model representations, which provides robustness against large leaf species variations as well as high inter-species and low intra-class similarity that occurs for some species. For the decision procedure, we use a two-stage Bayesian framework: the first concerns each shape model separately and the second is a combination of classification scores (posterior probabilities) obtained from each shape model. Experiments carried out on real world leaf images, the simple leaves of the Pl@ntLeaves scan images (46 species), show an increase in performance compared to previous related work.