A Fast Leaf Recognition Algorithm based on SVM Classifier and High Dimensional Feature Vector

Plants are fundamental for human beings, so it's very important to catalog and preserve all the plants species. Identifying an unknown plant species is not a simple task. Automatic image processing techniques based on leaves recognition can help to find the best features useful for plant representation and classification. Many methods present in literature use only a small and complex set of features, often extracted from the binary images or the boundary of the leaf. In this work we propose a leaf recognition method which uses a new features set that incorporates shape, color and texture features. A total of 138 features are extracted and used for training a SVM model. The method has been tested on Flavia dataset (Wu et al., 2007), showing excellent performance both in terms of accuracy that often reaches 100%, and in terms of speed, less than a second to process and extract features from an image.