Flower species recognition based on fusion of multiple features

As an important branch of plant automatic classification and recognition, flower species recognition is of great value in academic research and practical application. In view of the limitation of description methods for flower features and the problem of low accuracy of flower species recognition, flower images were used as recognition objects, the segmentation algorithm of Grab Cut based on saliency detection was used to preprocess the images with complex background and the images with single background were got. Then the color and shape features of the flower region were extracted. In addition to the color and shape features of the whole flower region, the color and shape features of the pistil/stamen area were also used to represent the flower characteristics more precisely. At last the 18 features were fused into a single feature vector and the SVM (support vector machine) was used for classification and recognition. Experiment based on self-built flower library was conducted for 360 images (15 images each for 24 different species), 240 images were used for the training samples, and the rest of 120 images were used for the test samples. Compared with the recognition methods based on the combination of different features, the method provided in this paper has achieved better recognition effect.The experimental results showed that the recognition accuracy reached 92.50%. Experiments based on Oxford 17 flower library were conducted for 510 images, in which 340 images were used for the training samples, and 170 images were used for the test samples. The experimental results show that the proposed method is effective.