Multi-resolution region-preserving segmentation for color images of natural scene

Image segmentation is one of the primary steps in image analysis for image labeling and retrieval. Recent Segmentation methods have shown a strong interest in graph based algorithm, and they have been quite successful in identifying significant regions and their boundaries. The cost functions used in these graph algorithms are usually based on low-level pixel-based image features such as position, intensity, and color. These methods tend to produce over-segmented results, especially for images of natural scenes whose regions contain complex but coherent mixture of colors. This thesis describes a multi-resolution segmentation algorithm which rst constructs a region pyramid that preserves the color distributions of regions, and then applies a graph cut algorithm at the top level of the pyramid to identify main regions in the image, and finally refines the region boundaries with a top-down approach based on integer linear programming. This way, main image regions are identified while over-segmentation is minimized.

Author dc:creator

• GUO JU GUI

• segmentation, image, color, multi-resolution, region-preserving, graph-cut