关键词: 金字塔梯度方向直方图, 差异性值, 支持向量机, 干旱区植物叶片

Abstract: The existing plant leaf identification methods are based on flat-shaped leaves, while the arid areas of plant leaves are more needles, so it is not suitable for the identification of plant leaves in arid areas, so that the plant researches in arid area rely mainly on expert identification, not conducive to the further study of plant leaves in arid area. A Local Linear Embedding Algorithm D-LLE is proposed using the difference value to fully excavate the classification information between the samples to improve the identification efficiency of plant leaves in arid area. The Pyramid Histograms of edge Orientation Gradients（PHOG） is used to extract the leaf image features, then reduces the dimension of the extracted PHOG features by using the mainstream dimensionality reduction algorithm, such as PCA, LLE, WLLE, D-LLE. Finally, the classification model of Support Vector Machine（SVM） is established to classify the plant leaf images. The average recognition rates of these four dimensionality reduction algorithms are 76.3%, 85.3%, 89.1%, 95.5%. Respectively, compare with other plant leaves, the correct recognition rates of the leaves of camel prickles, kudou and zizyphus jujuba are low. This proves that PHOG is feasible in feature extraction of plant leaves by experiments. Furthermore, the D-LLE algorithm is more efficient than the traditional feature reduction algorithms and more suitable for automatic recognition and classification of plant leaves in arid area.

Key words: Pyramid Histograms of edge Orientation Gradients（PHOG）, dissimilarity, Support Vector Machine（SVM）, plant leaves in area