Abstract Aiming at the fault segmentation caused by color density clustering segmentation model, a dominant colors clustering image segmentation algorithm is proposed based on visual saliency. Firstly, according to the spatial color information and Mean-shift smoothing results, the global saliency and region saliency of the image are computed and fused as the constraints of spatial clustering. Then, kernel density estimation is employed to compute dominant colors of image as initial clusters and the salient features are taken as regulated factors for clustering segmentation. Finally, regions are merged for final segmentation. The experiments are implemented on the standard segmentation database and the proposed algorithm is compared with several algorithms. The experimental results show the higher precision of the proposed algorithm on region contours. The proposed algorithm makes good use of the salient feature of image, reduces the inconsistency of the clustering results, and improves the accuracy of pixel clustering and the robustness of the segmentation.
Corresponding Authors:
Aizhong(Corresponding author), born in 1977, Ph.D., associate professor. His research interests include image segmentation and pattern recognition.
About author:: SIMA Haifeng, born in 1982, Ph.D., lecturer. His research interests include image segmentation and pattern recognition.MI WANG Zhiheng, born in 1983, Ph.D., associate professor. His research interests include pattern recognition and image processing.DU Shouheng, born in 1963, associate professor. His research interests include computer vision and image processing.
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2016, Vol. 29 
