Application of Intelligent Clustering Algorithm in Image Processing

In order to solve the limitation of traditional K-means algorithm in dealing with large-scale data, a fast approximate k-means algorithm (FAKM) is proposed based on the approximate k-means algorithm (AKM) and the idea of classifying the cluster centers. The algorithm omits the cluster centers which only obtain a few samples in the AKM clustering results, and makes full use of the cluster centers with dense and stable samples in the cluster, In the iterative process, the number of samples and categories to be clustered is gradually reduced, which improves the speed of the algorithm and simplifies the clustering results. The FAKM algorithm is applied to the actual image retrieval system, and the experimental results show that the retrieval accuracy, retrieval time and clustering time of the system are greatly improved.