Local Gabor Phase Quantization Scheme for Robust Leaf Classification

This paper presents the new feature extraction scheme for accurate leaf classification. The proposed feature extraction scheme can be viewed as a combination of Gabor transform and Local Phase Quantization (LPQ) and we term this scheme as Local Gabor Phase Quantization (LGPQ). First, the Gabor magnitude images are obtained by convolving the given leaf image with Gabor filter with different scale and orientation. Then, we divide each of these Gabor magnitude images into number of sub-images of size 10 × 10. Then, we encode each of these sub-images using LPQ to capture the rich set of information and concatenated to form a single feature set. We then, use Principal Component Analysis (PCA) to reduce the dimension of the feature space. Finally, the reduced feature set is classified using Support Vector Machine (SVM). Extensive experiments are carried out on three different datasets with varying size and illumination to prove the efficacy of the proposed scheme.