Research on Digital Image Recognition Algorithm Based on Pattern Recognition

This paper studies and designs digital image recognition algorithms based on pattern recognition. The feature vector includes four-dimensional feature vector, eight-dimensional feature vector and two-dimensional feature vector based on principal component analysis. The classification methods include K-nearest neighbor method, minimum distance method and fixed increment method. Through the combination of different feature vectors and different classification methods, different classification results are obtained. At the same time, the advantages, disadvantages and accuracy of three methods are compared. The results show that the K-nearest neighbor method has high accuracy, it is insensitive to abnormal points and easy to implement.