Segmentation of Multiple Salient Closed Contours from Real Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Quantitative Evaluation of a Novel Image Segmentation Algorithm
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Region saliency as a measure for colour segmentation stability
Image and Vision Computing
Salient region detection and segmentation
ICVS'08 Proceedings of the 6th international conference on Computer vision systems
Saliency detection for content-aware image resizing
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Global contrast based salient region detection
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
The JPEG2000 still image coding system: an overview
IEEE Transactions on Consumer Electronics
Unsupervised extraction of visual attention objects in color images
IEEE Transactions on Circuits and Systems for Video Technology
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In this paper, we propose a novel segmentation model integrated the salient regional features into mean shift (MS) clustering segmentation as fusion matrixes. Firstly, a regional visual saliency map of the given image is obtained based on quantification image in HSV color space. Then saliency factors are extracted from salience map from each channel in L*a*b space in two steps: region saliency(S-R) and pixels-region (P-R). Fuse the salient factors derived from former salient features with original components of the image as new input features, who are involved in the mean-shift procedure for segmentation. This paper takes advantage of regional salience to guide the MS vectors moving to accurate modes, and decreases premature and ill convergence at local area. The introduction of salient factors enhances the accuracy of the pixels clustering for region segment. Experiment results carried on Berkeley database and comparison with human segmentation results demonstrated that our algorithm has better performance on nature color images segmentation.