Unsupervised multiscale segmentation of color images
Pattern Recognition Letters
Color image segmentation using morphological clustering and fusion with automatic scale selection
Pattern Recognition Letters
Review article: Local adaptive receptive field self-organizing map for image color segmentation
Image and Vision Computing
Color image segmentation using an enhanced Gradient Network Method
Pattern Recognition Letters
Unsupervised colour image segmentation using dual-tree complex wavelet transform
Computer Vision and Image Understanding
Colour image segmentation using fuzzy clustering techniques and competitive neural network
Applied Soft Computing
Image segmentation via coherent clustering in L*a*b* color space
Pattern Recognition Letters
Color-Based Image Salient Region Segmentation Using Novel Region Merging Strategy
IEEE Transactions on Multimedia
Dominant color segmentation of administrative document images by hierarchical clustering
Proceedings of the 2013 ACM symposium on Document engineering
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An algorithm is presented to segment a color image based on the 3D histogram of colors. The peaks in the histogram, i.e., the connected components of colors with locally maximal occurrence, are detected. Each peak is associated a representative color, which is the color of the centroid of the peak. Peaks are processed in decreasing occurrence order, starting from the peak with the maximal occurrence, with the purpose of maintaining only the representative colors corresponding to the dominant peaks. To this aim, each analyzed peak groups under its representative color those colors, present in the histogram and that have not been grouped to any already analyzed peak, such that their distance from the centroid of the peak is smaller than a priori fixed value. At the end of the grouping process, a number of representative colors, generally substantially smaller than the number of initial peaks, is obtained, which are used to identify the regions into which the color image is segmented. Since the histogram does not take into account spatial information, the image is likely to result over-segmented and a merging step, based on the size of the segmentation regions, is performed to reduce this drawback.