Content-based Image retrieval has become an important part of information retrieval technology. Images can be viewed as high dimensional data and are usually represented by their low-level features. How to effectively find the semantic meanings of images is a central challenge in the area. In this paper, we propose an interactive platform for region-based image clustering and retrieval. A Genetic Algorithm is used to perform the initial clustering. In order to further refine the clustering results, we adopt the maximum flow/minimum cut theorem from graph theory to do outlier/outlier group detection. Outlier detection can help identify misclustered image segments and is used to improve the quality of clusters in this paper. In the interactive retrieval phase, user feedback is used to dynamically locate candidate images from clusters and outliers/outlier groups. Through Relevance Feedback, more information is gathered and fed to the learning algorith - One-class SVM. Experiments show the effectiveness of the proposed platform.