SPIE Journal Paper | 16 November 2023
KEYWORDS: Veins, Feature extraction, Image classification, Cooccurrence matrices, Classification systems, Shape analysis, Deep learning, Corner detection, Education and training, Detection and tracking algorithms
When conducting image classification, traditional and deep-learning based methods require extracting target features in different ways and then classifying targets based on the features. Therefore, extracting more informative and effective features is very important, and this problem also exists in plant leaf classification. To address the issue for traditional leaf classification, this article carefully designs two shape features [area ratio-based shape descriptor (ARSD) and corner-based shape descriptor (CSD)] and one leaf vein feature (LVF) extraction method based on statistical characteristics. Then, according to the extraction of ARSD, CSD, and LVF, a general feature extraction framework is formed, which first normalizes direction, segments region of interest, then divides it into subregions, counts the number of pixels in each sub region, and finally forms feature vectors. Finally, to visually verify the effectiveness of these features, they were applied to the classic leaf classification framework, which is adjusted to form a two-stage classification method from coarse classification of shapes and fine classification of species. In the first stage (coarse classification), leaves in three universal datasets [Flavia dataset, Swedish dataset and Intelligent Computing Laboratory (ICL) dataset] and a self-built LZU dataset are respectively divided into several shape categories only by the ARSD. Experiment results show that the ARSD has obvious advantages in shape-based rough classification. Then in the second stage (fine classification), shape, texture, vein, and color features, including ARSD, CSD, local binary patterns, gray-level co-occurrence matrix, the developed LVF, and color moments, are combined to subdivide finer species in each shape category. Extensive experiment results show that the classification accuracy on the Flavia, Swedish, ICL, and LZU datasets have reached 99.3%, 98.9%, 91.5%, and 95.3%, respectively, comparable to deep learning-based methods.
