A Pests Image Classification Method Based on Improved Wolf Pack Algorithm to Optimize Bayesian Network Structure Learning

Abstract

The traditional pests image recognition technology is based on the point features and line features of the image. In the case of complex lighting conditions or changing camera angles, the classification recognition effect is inaccurate. This article proposes a pests image classification method based on improved Wolf Pack Algorithm (WPA) to optimize Bayesian Network (BN) structure learning. Firstly, We select a pre-trained Convolutional Neural Network (CNN) to extract the image features of data set. And then input the feature vectors and classification of images into BN. Secondly, improved the traditional Wolf Pack Algorithm and used as a search algorithm, Bayesian Information Criterion (BIC) as a scoring function to learn the structure of BN. Then the parameters of BN are learned by Maximum Likelihood (ML) algorithm to form a Bayesian Classifier. Compared with other pest classification method, this method has a certain extent improvement in the classification accuracy of pest image classification.