Unsupervised Detection for Minimizing a Region of Interest around Distinct Object in Natural Images

Abstract

One of the major challenges for region-based image retrieval is to identify the Region of Interest (ROI) that comprises object queries. However, automatically identifying the regions or objects of interest in a natural scene is a very difficult task because the content is complex and can be any shape. In this paper, we present a novel unsupervised detection method to automatically and efficiently minimize the ROI in the images. We applied an edge-based active contour model that drew upon edge information in local regions. The mathematical implementation of the proposed active contour model was accomplished using a variational level set formulation. In addition, the mean-shift algorithm was used to reduce the sensitivity of parameter change of level set formulation. The results show that our method can overcome the difficulties of non-uniform sub-region and intensity in homogeneities in natural image segmentation.