Research on Image Clustering Algorithm Based on Multi-features Extraction

Image clustering is one of the classical problems in the field of machine learning and image processing. The extraction of image features is the most important aspect of image clustering. In view of the poor performance of traditional image feature extraction, the characteristics of various image feature extraction algorithms including SIFT, ORB, and color histogram are discussed. A proposed method is of preprocessing the image first, then performing multi-features extraction and fusion, finally proceeding clustering. At the same time, multiple groups of comparative experiments are carried out. It can be seen from the experimental results that both clustering accuracy and clustering speed are taken into account by the image clustering method. Among them, the clustering accuracy can reach 99%, which shows that this method has more advantages in image clustering tasks.