Approximate query processing for efficient content-based image retrieval based on a hierarchical SOM

In this paper, we propose a new approach for similarity matching in image retrieval based on approximate query processing in a hierarchical SOM. We first map the high dimensional input vectors to a low dimensional grid by a local membership function which preserves the relationships between the input vectors and their neighboring weight vectors. Then, we use a hierarchical tree to reduce the computation cost for finding the best match unit. Finally, we retrieve the k nearest neighbors of the query vector by an approximate query processing approach. The experiments show that the proposed approach works well on both synthetic datasets and image databases.