Plant species biometric using feature hierarchies
Thesis DisciplineComputer Science
Degree GrantorUniversity of Canterbury
Degree NameMaster of Science
Biometric identification is a pattern recognition based classification system that recognizes an individual by determining its authenticity using a specific physiological or behavioural characteristic (biometric). In contrast to number of commercially available biometric systems for human recognition in the market today, there is no such a biometric system for plant recognition, even though they have many characteristics that are uniquely identifiable at a species level. The goal of the study was to develop a plant species biometric using both global and local features of leaf images. In recent years, various approaches have been proposed for characterizing leaf images. Most of them were based on a global representation of leaf peripheral with Fourier descriptors, polygonal approximations and centroid-contour distance curve. Global representation of leaf shapes does not provide enough information to characterise species uniquely since different species of plants have similar leaf shapes. Others were based on leaf vein extraction using intensity histograms and trained artificial neural network classifiers. Leaf venation extraction is not always possible since it is not always visible in photographic images. This study proposed a novel approach of leaf identification based on feature hierarchies. First, leaves were sorted by their overall shape using shape signatures. Then this sorted list was pruned based on global and local shape descriptors. The consequent biometric was tested using a corpus of 200 leaves from 40 common New Zealand broadleaf plant species which encompass all categories of local information of leaf peripherals. Two novel shape signatures (full-width to length ratio distribution and half-width to length ratio distribution) were proposed and biometric vectors were constructed using both novel shape signatures, complex-coordinates and centroid-distance for comparison. Retrievals were compared and the biometric vector based on full-width to length ratio distribution was found to be the best classifier. Three types of local information of the leaf peripheral (leaf margin coarseness, stem length to blade length ratio and leaf tip curvature) and the global shape descriptor, leaf compactness, were used to prune the list further. The proposed biometric was able to successfully identify the correct species for 37 test images (out of 40). The proposed biometric identified all the test images (100%) correctly if two species were returned compared to the low recall rates of Wang et al. (2003) (30%, if 10 images were returned) and Ye et al. (2004) (71.4%, if top 5 images were returned). The biometric can be strengthened by adding reference images of new species to the database, or by adding more reference images of existing species when the reference images are not enough to cover the leaf shapes.