ORTHOGONAL MAXIMUM MARGIN DISCRIMINANT PROJECTION WITH APPLICATION TO LEAF IMAGE CLASSIFICATION

Abstract

A novel supervised dimensionality reduction method called orthogonal maximum margin discriminant projection (OMMDP) is proposed to cope with the high dimensionality, complex, various, irregular-shape plant leaf image data. OMMDP aims at learning a linear transformation. After projecting the original data into a low dimensional subspace by OMMDP, the data points of the same class get as near as possible while the data points of the different classes become as far as possible, thus the classification ability is enhanced. The main differences from linear discriminant analysis (LDA), discriminant locality preserving projections (DLPP) and other supervised manifold learning-based methods are as follows: (1) In OMMDP, Warshall algorithm is first applied to constructing both of the must-link and class-class scatter matrices, whose process is easily and quickly implemented without judging whether any pairwise points belong to the same class. (2) The neighborhood density is defined to construct the objective function of OMMDP, which makes OMMDP be robust to noise and outliers. Experimental results on two public plant leaf databases clearly demonstrate the effectiveness of the proposed method for classifying leaf images.