Research on image feature extraction method based on principal component analysis

Traditional image feature extraction methods often have problems such as high feature dimension and redundant information. This paper proposes an image feature extraction method based on principal component analysis (PCA) to solve these problems. Firstly, image data is preprocessed, including graying and normalization. Then, PCA technology is used to reduce the dimension of image features, extract the main feature components, and reduce the feature dimension to remove redundant information. Finally, K- means clustering algorithm is used to cluster and analyze the features after dimensionality reduction, and similar image features are classified into one category, so as to realize image classification and recognition. This paper also uses t-SNE feature extraction for comparison. Experiments on multiple image data sets show that PCA can effectively extract image features, improve the accuracy and efficiency of clustering, and has better performance in image classification and retrieval tasks, which provides an effective feature extraction method for image processing and computer vision.