A computer image feature extraction algorithm based on distributed K-means algorithm

In order to understand computer image feature extraction algorithms, a research on computer image feature extraction algorithm based on distributed K-means algorithm has been proposed. The feature extraction of images, as the key, fundamentally determines the accuracy of image recognition. With the advent of the big data era, feature learning methods represented by deep learning models have demonstrated their powerful capabilities in image feature extraction tasks. Secondly, through layer by layer training, deep learning models can extract deep features of images in a highly autonomous manner, and have achieved the best results in the current research stage. Although deep learning models have extremely powerful learning abilities, the complexity of their model structure and the overhead involved in training also hinder their training. Based on clustering algorithms, a hierarchical image feature extraction model is constructed to improve the accuracy of image classification.