Parallel Hierarchical K-means Clustering-Based Image Index Construction Method

Content-based image retrieval often uses the integration of various features. The characteristic dimensions are up to hundreds of dimensions. The capabilities for image representation, storage, management far exceed that of the database. Retrieval and matching of large-scale image are the urgent problems which need to be solved. To solve the construction of image indexing of large-scale image retrieval, this paper introduces a parallel level K-means clustering method. Firstly, image clustering based on the level K-means reduces the size of matching data in retrieval, secondly, considering the inherent defects in the cluster, we put forward optimization program and calculate cluster rapidly with parallel computing algorithms. The experimental results show that this method can quickly build a massive image index for fast image retrieval.