A New Flower Classification System Using LBP and SURF Features

Abstract

Flowers are blessing of nature. Classification of flowers as a natural image is difficult as they are surrounded by background. So a segmentation phase is needed to separate the flower from background as good as possible. Computer vision has gained much attention for classification task. This paper proposes a method to classify flower with the help of LBP and SURF as features and SVM as a classifier. Input image is pre-processed for enhancement of image quality. Then the image is segmented by applying active contour segmentation method. After segmentation of the image, LBP and SURF features are extracted. SURF features are extracted from MSER regions. Then both features are concatenated. These concatenated features are sent for classification to SVM classifier. Quadratic SVM is employed here. Quadratic SVM trains these feature and tests to classify. We also tried out with different classifier. But they provide poor results. Proposed quadratic SVM achieves an accuracy of 87.2% which is significant and comparable for this classification taskK.