基于K-SVD和正交匹配追踪稀疏表示的稻飞虱图像分类方法

针对当前稻飞虱图像分类研究中存在图像识别速度慢、分类精度低的不足,该文提出一种基于K-SVD和正交匹配追踪(orthogonal matching pursuit, OMP)稀疏表示的稻飞虱图像分类方法。首先,根据稻飞虱的趋光性特点,使用团队自主研发的野外昆虫图像采集装置自动获取稻田害虫图像;然后,利用K-SVD算法对稻飞虱图像特征的过完备字典进行更新构造,结合OMP算法对原始输入图像的特征信号进行稀疏表示;最后,通过求解输入图像的重构误差对昆虫图像进行分类。在相同的试验条件下,与传统的图像分类算法(SVM、BP神经网络)进行比较。实验结果表明,该文提出的基于K-SVD和OMP算法的稻飞虱图像稀疏表示分类方法可对稻飞虱与非稻飞虱进行快速准确的分类,分类速度达到6.0帧/s,平均分类精度达到93.7%。与SVM和BP神经网络相比,分类速度分别提高了5和5.5帧/s;分类精度分别提高了15.7和28.2个百分点,为稻飞虱的防治预警工作提供了信息与技术支持。

Sparse representation classification method of rice planthopper image based on K-SVD and orthogonal matching pursuit algorithm

Abstract: Rice is a staple crop in China. Controlling rice pests and diseases is important to safeguard its sustainable production, in which classification and identification of rice planthoppers plays an important part. While there has been an increased interest over the past few years in image-classification of the rice planthoppers, currently, this method is not automatic and susceptible to faulty recognition and low efficiency. To circumvent these shortcomings, a sparse-representation image-based classification method was proposed based on the K-SVD and OMP. A field insect collection device was used to collect insect images, in which a high-pressure mercury lamp was used to attract the insects to the collection workbench based on their phototaxis characteristics. A PLC was mounted on the top of the three-phase adjustment device to control high-definition industrial cameras to take images of the insects. The images were then segmented using the maximum inter-class variance method (OTSU threshold segmentation method) to extract the image of the insects. Overall, 1186 single insect images were obtained from the field experiment. Two insect characteristics were selected as initial over-complete dictionary and they were extracted from 500 images. K-SVD dictionary learning algorithm was used to iteratively update the over-complete dictionary of the rice planthopper image features and the number of iterations was set at 500. 200 images of the rice planthoppers were selected as the original input comparing images, and 150 and 50 images of the rice and non-rice planthopper were used as testing and verifying sets respectively. The appropriate sparsity is an important factor for improving efficiency and accuracy, and the errors of the testing images was calculated from the OMP algorithm when the sparsity was 6, 12 and 18 respectively. Comprehensive analysis of the errors and convergence rate showed that the optimal sparsity was 12. Finally, classification features of the rice and non-rice planthoppers were sparsely reconstructed in line with the updated reconstruction dictionary, and the reconstruction error was calculated for the sparsity of 12. Hundreds of experiments revealed that the classification threshold of 0.1 was quick and effective to classify the rice and non-rice planthopper. Using the same experimental data, we compared the proposed method with the traditional image-based classification algorithms, SVM and BP neural network. The results showed that the accuracy and classification speed were 65.5% and 0.5 frames/s respectively for SVM, and 78.0% and 1.0 frames/s respectively for the BP neural network. In contrast, the proposed method improved the accuracy to 93.7% and the classification speed to 6.0 frames/s