COLOR IMAGE SEGMENTATION BASED ON MEAN SHIFT AND NORMALIZED CUTS
Image segmentation is a process of dividing an image into different regions such that each region is nearly homogeneous, whereas the union of any two regions is not. It serves as a key in image analysis and pattern recognition and is a fundamental step toward the low-level vision, which is significant for object recognition and tracking, image retrieval, face detection, and other computer-vision-related applications. Color images carry much more information than gray-level ones. In many pattern recognition and computer vision applications, the color information can be used to enhance the image analysis process and improve segmentation results compared to gray-scale-based approaches. As a result, great efforts have been made in recent years to investigate the segmentation of color images due to demanding needs. In this correspondence, a novel approach that provides effective and robust segmentation of color images is developed using Matlab. By incorporating the advantages of the mean shift (MS) segmentation and the normalized cut (Ncut) partitioning methods, the proposed method requires low computational complexity and is therefore very feasible for real-time image segmentation processing. It pre-processes an image by using the MS algorithm to form segmented regions that preserve the desirable discontinuity characteristics of the image. The segmented regions are then represented by using the graph structures, and the Ncut method is applied to perform globally optimized clustering. Because the number of the segmented regions is much smaller than that of the image pixels, the proposed method allows a low-dimensional image clustering with a significant reduction of the complexity compared to conventional graph partitioning methods that are directly applied to the image pixels. In addition, the image clustering using the segmented regions, instead of the image pixels, also reduces the sensitivity to noise and results in enhanced image segmentation performance. Furthermore, to avoid some inappropriate partitioning when considering every region as only one graph node, we develop an improved segmentation strategy using multiple child nodes for each region. The superiority of the proposed method is examined and demonstrated through a large number of experiments using color natural scene images.
Reference Paper: Color Image Segmentation Based on Mean Shift and Normalized Cuts
Author’s Name: Wenbing Tao, Hai Jin, and Yimin Zhang
Source: IEEE
Year:2007
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