SUMMARY
Recent years have witnessed great interest in developing methods for content-based image retrieval (CBIR). Generally, the image search results which are returned by an image search engine contain multiple topics, and organizing the results into different clusters will facilitate users’ browsing. Our aim in this research is to optimize image searching time for a general image database. The proposed procedure consists of two steps. First, it represents each image with a data structure which is based on quadtrees and represented by multi-level feature vectors. The similarity between images is evaluated through the distance between their feature vectors; this distance metric reduces the query processing time. Second, response time is further improved by using a secondary clustering technique to achieve high scalability in the case of a very large image database.