Ensemble feature selection approach based on feature ranking for rice seed images classification

Abstract

In smart agriculture, rice variety inspection systems based on computer vision need to be used for recognizing rice seeds instead of using technical experts. In this paper, we have investigated three types of local descriptors, such as Local Binary Pattern (LBP), Histogram of Oriented Gradients (HOG) and GIST to characterize rice seed images. However, this approach raises the curse of dimensionality phenomenon and needs to select the relevant features for a compact and better representation model. A new ensemble feature selection is proposed to represent all useful information collected from different single feature selection methods. The experimental results have shown the efficiency of our proposed method in terms of accuracy.