title: A Novel Method of Automatic Plant Species Identification Using Sparse Representation of Leaf Tooth Features.
abstract: Automatic species identification has many advantages over traditional species identification. Currently, most plant automatic identification methods focus on the features of leaf shape, venation and texture, which are promising for the identification of some plant species. However, leaf tooth, a feature commonly used in traditional species identification, is ignored. In this paper, a novel automatic species identification method using sparse representation of leaf tooth features is proposed. In this method, image corners are detected first, and the abnormal image corner is removed by the PauTa criteria. Next, the top and bottom leaf tooth edges are discriminated to effectively correspond to the extracted image corners; then, four leaf tooth features (Leaf-num, Leaf-rate, Leaf-sharpness and Leaf-obliqueness) are extracted and concatenated into a feature vector. Finally, a sparse representation-based classifier is used to identify a plant species sample. Tests on a real-world leaf image dataset show that our proposed method is feasible for species identification.
authors.affiliation: School of Information Science and Engineering, Xiamen University, Xiamen, 361005, China.
authors.forename: Taisong
authors.initials: T
authors.lastname: Jin
authors.affiliation: School of Life Sciences, Xiamen University, Xiamen, 361005, China.
authors.forename: Xueliang
authors.initials: X
authors.lastname: Hou
authors.affiliation: School of Information Science and Engineering, Xiamen University, Xiamen, 361005, China.
authors.forename: Pifan
authors.initials: P
authors.lastname: Li
authors.affiliation: School of Information Science and Engineering, Xiamen University, Xiamen, 361005, China.
authors.forename: Feifei
authors.initials: F
authors.lastname: Zhou
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D018515:Plant Leaves / Q000033:anatomy & histology*