Plant Leaf Recognition Based on Contourlet Transform and Support Vector Machine

Plants are essential to the balance of nature and in people’s lives as the fundamental provider for food, oxygen and energy. The study of plants is also essential for environmental protection and helping farmers increase the production of food. As a fundamental task in botanical study, plant leaf recognition has been a hot research topic in these years. In this paper, we propose a new method based on contourlet transform and Support Vector Machine (SVM) for leaf recognition. Contourlet Transform is a promising multi-resolution analysis technique, which provides image with a flexible anisotropy and directional expansion. By basing its constructive principle on a non-subsampled pyramid structure and related directional filter banks, contourlet transform decomposes input images into multi-scale factors which also enjoys additional advantages such as shift invariance and computational efficiency. Compared with one-dimensional transforms, such as the Fourier and wavelet transforms, Contourlet Transform can capture the intrinsic geometrical structure. In order to ameliorate the influence of unwanted artefacts such as illumination and translation variations, in this paper, the contourlet transform was firstly applied to extract feature with high discriminative power. Then the extracted features are classified by SVM. The experimental results show that the proposed method has high sensitivity of directionality and can better capture the rich features of natural images such as edges, curves and contours.