Robust multi-view latent low rank representation algorithm for image classification
With the rapid development of 5G and network technology,a large number of internet images have appeared in people′s vi-sion.The high-dimension and noise characteristics of Internet images are the main challenges in classification problems.In order to im-prove the recognition and robustness of Internet images,this study proposes a robust multi-view latent low rank representation(RMLL-RR)algorithm for image classification.The RMLLRR algorithm incorporates the idea of multi-view learning within the framework of low rank representation algorithm.Based on the complementary and consistent criteria of multiple views,it utilizes multiple features to ob-tain comprehensive image description information,maximizing consistency between different views and minimizing divergence in infor-mation description between views.The RMLLRR algorithm uses the idea of latent low rank representation,filters redundant features and noise information,and focuses on the principal feature and salient feature of the image,making the model more robust and discrim-inative.In addition,the RMLLRR algorithm utilizes the ε-draggings technique to learn the relaxed label matrix with large intervals be-tween classes,which enhances the discrimination ability of classes.The experimental results of the face dataset ORL,object dataset COIL,and object recognition dataset GRAZ show that in noisy environments,the RMLLRR algorithm achieves the best classification results among all compared algorithms,with classification accuracy of 92.43%,98.95%,and 63.37%,respectively.
万方数据
维普
