Quantitative evaluation of color image segmentation results
Pattern Recognition Letters
Color image processing and applications
Color image processing and applications
Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Introduction to MPEG-7: Multimedia Content Description Interface
Introduction to MPEG-7: Multimedia Content Description Interface
Unsupervised color-texture segmentation based on soft criterion with adaptive mean-shift clustering
Pattern Recognition Letters
Robust target detection and tracking through integration of motion, color, and geometry
Computer Vision and Image Understanding
Image segmentation by unsupervised sparse clustering
Pattern Recognition Letters
A survey of content-based image retrieval with high-level semantics
Pattern Recognition
Segmentation and tracking of multiple video objects
Pattern Recognition
A color image segmentation approach for content-based image retrieval
Pattern Recognition
Toward Objective Evaluation of Image Segmentation Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
A variational formulation for segmenting desired objects in color images
Image and Vision Computing
Color image segmentation guided by a color gradient network
Pattern Recognition Letters
Region saliency as a measure for colour segmentation stability
Image and Vision Computing
A fast MPEG-7 dominant color extraction with new similarity measure for image retrieval
Journal of Visual Communication and Image Representation
The use of attention and spatial information for rapid facial recognition in video
Image and Vision Computing
A hierarchical approach to color image segmentation using homogeneity
IEEE Transactions on Image Processing
Spatiotemporal video segmentation based on graphical models
IEEE Transactions on Image Processing
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This paper presents a clustering-based color segmentation method where the desired object is focused on. As classical methods suffer from a lack of robustness, salient colors appearing in the object are used to intuitively tune the algorithm. These salient colors are extracted according to a psychovisual scheme and a peak-finding step. Results on various test sequences, covering a representative set of outdoor real videos, show the improvement when compared to a simple implementation of the same K-means oriented segmentation algorithm with ad hoc parameter setting strategy and with the well-known mean-shift algorithm.