Statistical clustering is critical in designing scalable image retrieval systems. In this paper, we present a scalable algorithm for indexing and retrieving images based on region segmentation. The method uses statistical clustering on region features and IRM (Integrated Region Matching), a measure developed to evaluate overall similarity between images that incorporates properties of all the regions in the images by a region-matching scheme. Compared with retrieval based on individual regions, our overall similarity approach (a) reduces the influence of inaccurate segmentation, (b) helps to clarify the semantics of a particular region, and (c) enables a simple querying interface for region-based im- age retrieval systems. The algorithm has been implemented as a part of our experimental SIMPLI city image retrieval system and tested on large-scale image databases of both general-purpose images and pathology slides. Experiments have demonstrated that this technique maintains the accuracy and robustness of the original system while reducing the matching time significantly.