基于可区分二进制局部模式特征的蛾类昆虫识别

摘要

Most of insects of moth species are the agricultural pests,therefore the automatic recognition of the category of insects of moth species are of great importance for pest prediction.Current automatic insect recognition methods mostly focus on recognising the insects above the order level but are hard to recognise the insects on the order level,in light of this problem we proposed a texture feature-based recognition method for insects of moth species.It extracts the features of insect images by applying a modified local binary pattern algorithm (CLBP),and distils nature dimension from the extracted feature matrix according to the category of insects of moth species,and finally applies KNN algo-rithm to the realisation of recognising insects of moth species.Experimental results showed that the discriminative CLBP can extract features from original insect images directly and achieves excellent recognition performance,meanwhile it can effectively reduce the dimensions of fea-tures matrix,so that dwindles the storage space and decreases the computational complexity of similarity comparison.Our research broadens the application scope of computer vision and will be helpful for early prediction of agriculture pests and diseases.%蛾类昆虫多为农业害虫，自动识别蛾类昆虫种类对虫害预测预报意义重大。针对现有昆虫自动识别方法集中在目以上层次昆虫识别难以实现目内昆虫识别，提出一种基于纹理特征的蛾类昆虫识别方法。应用一种改进的局部二进制模式提取昆虫图像特征，对提取得到的特征矩阵按照蛾类昆虫类别抽取本质维数，最后用 KNN 算法实现蛾类昆虫识别。实验结果表明：可区分CLBP 能够对昆虫原始图像直接提取特征并获得优异的识别性能，同时，能够有效降低特征矩阵的维数，从而缩小存储空间，降低相似性比较的计算复杂度。该研究拓宽了计算机视觉技术应用范围，有助于实现农业病虫害的尽早预测。