A novel texture feature based multiple classifier technique for roadside vegetation classification
Proposed technique use LBP based GLCM feature vector and multiple classifiers. We achieve over 92% accuracy for vegetation classification. Extensive experiments use 5-fold cross validation. The experiments were conducted on dense and sparse grasses. In future, extension will be done by introducing large dataset of grasses.
feature extraction support vector machine k-nearest neighbor neural network hybrid technique