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.

A fast leaf recognition algorithm based on SVM classifier and high dimensional feature vector

DI RUBERTO, CECILIA;PUTZU, LORENZO
2014-01-01

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 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.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11584/107038
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