Color texture segmentation based on the modal energy of deformable surfaces
IEEE Transactions on Image Processing
ICIC'09 Proceedings of the Intelligent computing 5th international conference on Emerging intelligent computing technology and applications
A unified tensor level set for image segmentation
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on game theory
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A color- and texture-based image segmentation algorithm
Machine Graphics & Vision International Journal
Proceedings of the Seventh Indian Conference on Computer Vision, Graphics and Image Processing
EURASIP Journal on Advances in Signal Processing - Special issue on theory and application of general linear image processing
Color image segmentation using a semi-wrapped gaussian mixture model
PReMI'11 Proceedings of the 4th international conference on Pattern recognition and machine intelligence
A review on automatic image annotation techniques
Pattern Recognition
Image segmentation using normalized cuts and efficient graph-based segmentation
ICIAP'11 Proceedings of the 16th international conference on Image analysis and processing - Volume Part II
Color image segmentation using parallel OptiMUSIG activation function
Applied Soft Computing
Color texture segmentation based on image pixel classification
Engineering Applications of Artificial Intelligence
Automatic spectral video matting
Pattern Recognition
Spectral Image Segmentation Using Image Decomposition and Inner Product-Based Metric
Journal of Mathematical Imaging and Vision
Artistic minimal rendering with lines and blocks
Graphical Models
Structural image retrieval using automatic image annotation and region based inverted file
Journal of Visual Communication and Image Representation
QUAC: Quick unsupervised anisotropic clustering
Pattern Recognition
A new evaluation measure for color image segmentation based on genetic programming approach
Image and Vision Computing
Automatic image segmentation using constraint learning and propagation
Digital Signal Processing
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In this correspondence, we develop a novel approach that provides effective and robust segmentation of color images. 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 preprocesses 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 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.