International Journal of Computer Vision
Color Image Segmentation using Competitive Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
A comparison of clustering algorithms applied to color image quantization
Pattern Recognition Letters - special issue on pattern recognition in practice V
Segmentation of Color Textures
IEEE Transactions on Pattern Analysis and Machine Intelligence
Foundations of Mobile Radio Engineering
Foundations of Mobile Radio Engineering
Digital Image Processing
Markov Random Field Models for Unsupervised Segmentation of Textured Color Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Automatic watershed segmentation of randomly textured color images
IEEE Transactions on Image Processing
A new approach to image retrieval with hierarchical color clustering
IEEE Transactions on Circuits and Systems for Video Technology
Unsupervised video object segmentation and tracking based on new edge features
Pattern Recognition Letters
Remote sensing image fusion based on adaptive RBF neural network
ICONIP'06 Proceedings of the 13th international conference on Neural Information Processing - Volume Part II
Detection of disease using block-based unsupervised natural plant leaf color image segmentation
SEMCCO'11 Proceedings of the Second international conference on Swarm, Evolutionary, and Memetic Computing - Volume Part I
Expert Systems with Applications: An International Journal
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A new method for color textured segmentation using feature distributions is proposed in this study. First, the distributions of color and local edge patterns are used to derive a homogeneity measure for color textured regions. Then, a coarse-to-fine method based on the homogeneity measure is employed to achieve the goal of color texture segmentation. The proposed method unifies color and edge features to solve the color texture segmentation problem rather than simply extend gray-level texture analysis to color images, or analyze only spatial interaction of colors in a neighborhood. In addition, the proposed method is simple but effective. The execution time is fast, the error rate for collages of real texture is low and the segmentation results for natural scenes are visually satisfactory. Finally, no a priori knowledge about the number and types of textures or the number of regions are required in the proposed method. The feasibility and effectiveness of the proposed method have been demonstrated by various experiments.