Subspace Analysis Technique on Features Images in Leaf Species Recognition

Abstract

The aim of the proposed work is to classify the leaf species from an image of a leaf with computer vision. For this the input image will be processed with image processing algorithms, analyzed to extract features and then classified based on the extracted features with the help of pattern analysis and recognition techniques. We present a robust leaf species recognition system based on Gabor wavelet. Multiple Gabor wavelets with different scale and orientations are used to extract features. To have robust discriminative features which will also reduce the computational complexity and memory cost, we have used Gabor wavelet with selected scale and orientations only. For this selection, we performed number of experiments taking a varied number of scales and orientations. The feature vector so obtained has high dimensionality. The feature dimension is reduced by down sampling & subspace analysis. The kernel based subspace analysis is used to extract the nonlinear leaf variations. The performance of the proposed system is extensively tested using the Folio database. The results show a considerable saving in terms of computational cost and improvement in recognition rate compared to the contemporary related work.