A Model of Saliency-Based Visual Attention for Rapid Scene Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Saliency, Scale and Image Description
International Journal of Computer Vision
Models of bottom-up and top-down visual attention
Models of bottom-up and top-down visual attention
Two-Dimensional PCA: A New Approach to Appearance-Based Face Representation and Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Contrast-based image attention analysis by using fuzzy growing
MULTIMEDIA '03 Proceedings of the eleventh ACM international conference on Multimedia
Gaze-based interaction for semi-automatic photo cropping
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
An Integrated Model of Top-Down and Bottom-Up Attention for Optimizing Detection Speed
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Natural Image Statistics: A Probabilistic Approach to Early Computational Vision.
Natural Image Statistics: A Probabilistic Approach to Early Computational Vision.
Salient region detection and segmentation
ICVS'08 Proceedings of the 6th international conference on Computer vision systems
Is bottom-up attention useful for object recognition?
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Image Signature: Highlighting Sparse Salient Regions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Visual saliency detection by spatially weighted dissimilarity
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Global contrast based salient region detection
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
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In this paper, we propose a new multiscale saliency detection algorithm based on image patches. To measure saliency of pixels in a given image, we segment the image into patches by a fixed scale and then use principal component analysis to reduce the dimensions which are noises with respect to the saliency calculation. The dissimilarities between a patch and other patches, which indicate the patch's saliency, are computed based on the dissimilarity of colors and the spatial distance. Finally, we implement our algorithm through multiple scales that further decrease the saliency of background. Our method is compared with other saliency detection approaches on two public image datasets. Experimental results show that our method outperforms the state-of-the-art methods on predicting human fixations and salient object segmentation.