Enhanced Image Texture Feature Extraction Method Using Local Tetra Patterns for Plant Leaf Classification System

Image textures are groups of metrics computed to classify the captured texture of images. It reveals the information about the spatial orientation of color or gray level intensities in the images or specific regions of the images. The image texture classification of plant leaves is considered in this paper because of the extinction risk of various plants. An efficient plant leaf identification using image texture classification would aid in automatic recognition of the plant leaves. The existing image texture classification methods, namely, Local Tetra Patterns (LTrPs) are considered. LTrP considered only horizontal and vertical directions during the phasor difference computation stage. A Enhanced LTrP (ELTrP) is proposed in this paper by including the diagonal direction along with the horizontal and vertical directions in the LTrP. Then, features are extracted for the output image texture patterns. The features are classified using Support Vector Machine (SVM). 750 leaf images are trained from 50 classes, where each class consists of 15 leaves. 2500 leaf images are tested from 50 classes, where each class consists of 50 leaves. The proposed method ELTrP is analyzed in terms of classification accuracy with LBP, LTrP. Its performance is found to be satisfactory compared to LTrP.