The quickhull algorithm for convex hulls
ACM Transactions on Mathematical Software (TOMS)
A Model of Saliency-Based Visual Attention for Rapid Scene Analysis
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
Efficient Graph-Based Image Segmentation
International Journal of Computer Vision
Visual attention detection in video sequences using spatiotemporal cues
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
An eye fixation database for saliency detection in images
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part IV
Is bottom-up attention useful for object recognition?
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Learning to Detect a Salient Object
IEEE Transactions on Pattern Analysis and Machine Intelligence
Affective saliency map considering psychological distance
Neurocomputing
A saliency detection model based on local and global kernel density estimation
ICONIP'11 Proceedings of the 18th international conference on Neural Information Processing - Volume Part I
Saliency estimation using a non-parametric low-level vision model
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
Saliency filters: Contrast based filtering for salient region detection
CVPR '12 Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Center-surround divergence of feature statistics for salient object detection
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
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This paper proposes a novel salient region detection method based on background contrast. Salient regions are widely considered as those distinct to the image background. However, owning to the lack of information about the image background, existing methods measure saliency of an image pixel/region using its contrast to local neighborhoods or the entire image rather than the image background. Inappropriate contrast region leads to difficulty in highlighting the whole large salient region. In this paper, we discover that the absence of eye fixations provides an important clue to the image background. Regions without eye fixations are very likely the image background. To further acquire the spatial extent of image background, the complementary area of the convex-hull of eye fixations is used to represent the possible image background. Then we measure the saliency of each region by computing its contrast to the estimated image background. The experimental results demonstrate that our approach outperforms previous state-of-the-art methods.