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. While many interesting image segmentation techniques have been proposed, their performance in practical applications remains limited. Thus, integrating related regions into meaningful objects becomes a promising alternative. In this paper, 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.