Among representative content-based image retrieval schemes, region-based retrieval has shown promise in retrieving similar images that exhibit considerable local variations. However, since humans are accustomed to relying on object-level concepts rather than low-level regions, robust and accurate object segmentation is an essential step. We propose a new multiple-region level image retrieval algorithm based on region-level image segmentation and its spatial relationship. To capture spatial similarity, we apply Hausdorff distance (HD) to our region-based image retrieval system, FRIP (finding region in the pictures). In contrast to other object or multiple region-based retrieval systems, we update classical HD to retrieve similar regions regardless of their spatial translation, insertion, and deletion. Furthermore, we incorporate relevance feedback to reflect the user's high-level query and subjectivity to the system and to compensate for performance degradation due to imperfect image segmentation. The efficacy of our method is validated using a set of 3000 images from Corel-photo CD.