基于快速低秩编码与局部约束的图像分类算法  

Image classification algorithm based on fast low rank coding and local constraint

作　　者：甘玲[1] 左永强 

机构地区：[1]重庆邮电大学计算机科学与技术学院,重庆400065

出　　处：《计算机应用》2017年第10期2912-2915,共4页journal of Computer Applications

基　　金：国家自然科学基金资助项目(61272195)~~

摘　　要：针对快速低秩编码算法存在特征重建误差较大,以及特征间局部约束条件丢失的问题,提出一种强化局部约束的快速低秩编码算法。首先,使用聚类算法对图像中特征进行聚类,得到局部相似特征集合及其对应的聚类中心;其次,在视觉词典中采取K最近邻(KNN)策略查找聚类中心对应的K个视觉单词,并将其组成对应的视觉词典;最后,使用快速低秩编码算法获得局部相似特征集合对应的特征编码。改进算法在Scene-15和Caltech-101图像库上的分类准确率比快速低秩编码算法提高4%到8%,编码效率比稀疏编码算法提高5~6倍。实验结果表明,改进算法使得局部相似特征具有相似编码,从而更加准确地表达图像内容,能有效提高分类准确率及编码效率。Aiming at the problem of large feature reconstruction error and local constraint loss between features in fast low rank coding algorithm, an enhanced local constraint fast low rank coding algorithm was put forward. Firstly, the clustering algorithm was used to cluster the features in the image, and obtain the local similarity feature set and the corresponding clustering center. Secondly, the K visual words were found by using the K Nearest Neighbor （KNN） strategy in the visual dictionary, and then the K visual words were combined into the corresponding visual dictionary. Finally, the corresponding feature code of the local similarity feature set was obtained by using the fast low rank coding algorithm. On Scene-15 and Caltech-101 image datasets, the classification accuracy of the modified algorithm was improved by 4% to 8% compared with the original fast low rank coding algorithm, and the coding efficiency was improved by 5 to 6 times compared with sparse coding. The experimental results demonstrate that the modified algorithm can make local similarity features have similar codes, so as to express the image content more accurately, and improve the classification accuracy and coding efficiency.

关 键 词：图像分类 局部约束 低秩编码 特征编码 相似特征 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]