Fast image retrieval based on K-means clustering and multi-resolution data structure for large image databases

This paper presents a fast search algorithm based on multi-resolution data structure for efficient image retrieval in large image databases. The proposed algorithm consists of two stages: a database-building stage and a searching stage. In the database-building stage, we partition the image data set into a pre-defined number of clusters by using the MacQueen K-means clustering algorithm. The searching stage has the two steps to choose proper clusters and to find the best match among all the images included in the chosen clusters. In order to reduce the heavy computational cost in the searching stage, we propose two kinds of fast exhaustive searching algorithms based on the multi-resolution feature space, which guarantee a perfect retrieval accuracy of 100%. By applying these two algorithms to the searching stage, we can find the best match with very high search speed and accuracy. In addition, we consider a retrieval scheme producing multiple output images including the best match. Intensive simulation results show that the proposed schemes provide a prospective search performance.