Multi-Channel Mapping Image Segmentation Method Based on LDA

In order to improve the segmentation accuracy of plant lesion images, multi-channels segmentation algorithm of plant disease image was proposed based on linear discriminant analysis (LDA) method's mapping and K-means' clustering. Firstly, six color channels from RGB model and HSV model were obtained, and six channels of all pixels were laid out to six columns. Then one of these channels was regarded as label and the others were regarded as sample features. These data were grouped for linear discrimination analysis, and the mapping values of the other five channels were applied to the eigen vector space according to the first three big eigen values. Secondly, the mapping value was used as the input data for K-means and the points with minimum and maximum pixel values were used as the initial cluster center, which overcame the randomness for selecting the initial cluster center in K-means. And the segmented pixels were changed into background and foreground, so that the proposed segmentation method became the clustering of two classes for background and foreground. Finally, the experimental result showed that the segmentation effect of the proposed LDA mapping-based method is better than those of K-means, ExR and CIVE methods.