Authors:
Cecilia Di Ruberto
and
Lorenzo Putzu
Affiliation:
University of Cagliari, Italy
Keyword(s):
Image Analysis, Feature Extraction, Leaf Recognition, Plant Classification, Support Vector Machine.
Related
Ontology
Subjects/Areas/Topics:
Color and Texture Analyses
;
Computer Vision, Visualization and Computer Graphics
;
Features Extraction
;
Image and Video Analysis
;
Image Formation and Preprocessing
;
Image Formation, Acquisition Devices and Sensors
;
Shape Representation and Matching
Abstract:
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 of a SVM model. The method has been tested on Flavia dataset, 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.