Architecture Based Classification of Leaf Images
This work addresses the difficult task of plant identification for botanists and computer engineers by combining image features with botanical knowledge, though it appears incremental.
The paper tackles the problem of plant classification by proposing a systematic approach to extract leaf architecture features from digital images, achieving promising results on the ImagerCLEF 2012 dataset.
Plant classification and identification has so far been an important and difficult task. In this paper, an efficient and systematic approach for extracting the leaf architecture characters from captured digital images is proposed. The input image is first pre-processed in five steps to be prepared for feature extraction. In the second stage, methods for extracting different architectural features are studied using various mathematical and computational methods. Also, the classification rules for mapping the calculated values of each feature to semantic botanical terms in proposed. Compared with previous studies, the proposed method combines extracted features of an image with specific knowledge of leaf architecture in the domain of botany to provide a comprehensive framework for both computer engineers and botanist. Finally, Based on the proposed method, experiments on the classification of the ImagerCLEF 2012 dataset has been performed with promising results.