Plant recognition based on intersecting cortical model

• Published in: 2014 International Joint Conference on Neural Networks (IJCNN) pp. 975 - 980
• Main Authors: Zhaobin Wang, Xiaoguang Sun, Yide Ma, Hongjuan Zhang, Yurun Ma, Weiying Xie, Yaonan Zhang
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.07.2014
• Subjects: Accuracy; Biological system modeling; classification; Entropy; Feature extraction; ICM; leaf image; Neurons; plant recognition; Principal component analysis; Support vector machines
• ISSN: 2161-4393; 2161-4407
• DOI: 10.1109/IJCNN.2014.6889656

Plant recognition recently becomes more and more attractive in computer vision and pattern recognition. Although some researchers have proposed several methods, their accuracy is not satisfactory. Therefore, a novel method of plant recognition based on leaf image is proposed in the paper. Both shape and texture features are employed in the proposed method Texture feature is extracted by intersecting cortical model, and shape feature is obtained by the representation of center distance sequence. Support vector machine is employed for the classifier. The leaf image is preprocessed to get better quality for extracting features, and then entropy sequence and center distance sequence are obtained by intersecting cortical model and center distance transform, respectively. Redundant data of entropy sequence vector and center distance are reduced by principal component analysis. Finally, feature vector is imported into the classifier for classification. In order to evaluate the performance, several existing methods are used to compare with the proposed method and three leaf image datasets are taken as test samples. The experimental result shows the proposed method gets the better accuracy of recognition than other methods.