Analysis on low-resolution image correlation pattern recognition algorithm in finite space

Abstract

Because traditional correlation pattern recognition algorithm cannot accurately identify low-resolution images, a low-resolution image correlation pattern recognition algorithm based on block-centric symmetric local binary model and weighted principal component analysis algorithm is proposed. Firstly, the feature of low-resolution image is extracted by using block CS-LBP operator. The weighted PCA operator is used to reduce the feature, and the classification feature is obtained. Secondly, the optimal classification class is selected by the nearest neighbor classifier, the recognition rate is calculated, and the whole algorithm is analyzed. Finally, the experiment is carried out. The experimental results showed that the algorithm has good recognition effect on ORL, and it is robust to the change of light and weather.

Subject Classification:

• MSC2010
• 94C99

Keywords:

• Finite space
• Low-resolution
• Image association pattern
• Recognition algorithm