ROI-SEG: Unsupervised Color Segmentation by Combining Differently Focused Sub Results

This paper presents a novel unsupervised color segmentation scheme named ROI-SEG, which is based on the main idea of combining a set of different sub-segmentation results. We propose an efficient algorithm to compute sub-segmentations by an integral image approach for calculating Bhattacharyya distances and a modified version of the maximally stable extremal region (MSER) detector. The sub-segmentation algorithm gets a region-of-interest (ROI) as input and detects connected regions having similar color appearance as the ROI. We further introduce a method to identify ROIs representing the predominant color and texture regions of an image. Passing each of the identified ROIs to the sub-segmentation algorithm provides a set of different segmentations, which are then combined by analyzing a local quality criterion. The entire approach is fully unsupervised and does not need a priori information about the image scene. The method is compared to state-of-the-art algorithms on the Berkeley image database, where it shows competitive results at reduced computational costs.