关键词: 图像分类, 局部约束, 低秩编码, 特征编码, 相似特征

Abstract: 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.

Key words: image classification, local constraint, low rank coding, feature coding, similar feature