Color Image Segmentation using Competitive Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised Segmentation of Color-Texture Regions in Images and Video
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Markov Random Field Models for Unsupervised Segmentation of Textured Color Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computer Vision and Image Understanding
Linear color segmentation and its implementation
Computer Vision and Image Understanding - Special issue on color for image indexing and retrieval
IEEE Transactions on Signal Processing
Automatic watershed segmentation of randomly textured color images
IEEE Transactions on Image Processing
Fuzzy homogeneity approach to multilevel thresholding
IEEE Transactions on Image Processing
Histogram-based segmentation in a perceptually uniform color space
IEEE Transactions on Image Processing
Regions adjacency graph applied to color image segmentation
IEEE Transactions on Image Processing
Extraction of Illumination Effects from Natural Images with Color Transition Model
ISVC '08 Proceedings of the 4th International Symposium on Advances in Visual Computing, Part II
A faster graph-based segmentation algorithm with statistical region merge
ISVC'06 Proceedings of the Second international conference on Advances in Visual Computing - Volume Part II
Dominant color segmentation of administrative document images by hierarchical clustering
Proceedings of the 2013 ACM symposium on Document engineering
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In this paper, we present a new color image segmentation scheme based on unsupervised pixel classification that works even when there is not a one-to-one correspondence between the clusters of color points in the color space and the regions in the image. When the color points of different regions in the image give rise to a single cluster in the color space, the proposed scheme splits this cluster into sub-populations of color-points defined by color-domains. For this purpose, the connectedness and the color homogeneity properties of color-subsets of pixels defined by these color-domains are analyzed in order to construct the classes which correspond to the actual regions in the image. For selecting efficient color-domains, we propose a new concept, the spatial-color compactness degree, which evaluates the confidence that can be placed in the event ''the color-subset defined by the color-domain being examined corresponds to an actual region in the image''.