New geodesic distance transforms for gray-scale images
Pattern Recognition Letters
A Model of Saliency-Based Visual Attention for Rapid Scene Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Visual attention detection in video sequences using spatiotemporal cues
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
GeoS: Geodesic Image Segmentation
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
A comparative study of image retargeting
ACM SIGGRAPH Asia 2010 papers
An eye fixation database for saliency detection in images
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part IV
Superpixels and supervoxels in an energy optimization framework
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part V
Center-surround divergence of feature statistics for salient object detection
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
Con-text: text detection using background connectivity for fine-grained object classification
Proceedings of the 21st ACM international conference on Multimedia
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Generic object level saliency detection is important for many vision tasks. Previous approaches are mostly built on the prior that "appearance contrast between objects and backgrounds is high". Although various computational models have been developed, the problem remains challenging and huge behavioral discrepancies between previous approaches can be observed. This suggest that the problem may still be highly ill-posed by using this prior only. In this work, we tackle the problem from a different viewpoint: we focus more on the background instead of the object. We exploit two common priors about backgrounds in natural images, namely boundary and connectivity priors, to provide more clues for the problem. Accordingly, we propose a novel saliency measure called geodesic saliency. It is intuitive, easy to interpret and allows fast implementation. Furthermore, it is complementary to previous approaches, because it benefits more from background priors while previous approaches do not. Evaluation on two databases validates that geodesic saliency achieves superior results and outperforms previous approaches by a large margin, in both accuracy and speed (2 ms per image). This illustrates that appropriate prior exploitation is helpful for the ill-posed saliency detection problem.