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
Automatic thumbnail cropping and its effectiveness
Proceedings of the 16th annual ACM symposium on User interface software and technology
Automatic image retargeting with fisheye-view warping
Proceedings of the 18th annual ACM symposium on User interface software and technology
Video retargeting: automating pan and scan
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
Efficient Subwindow Search: A Branch and Bound Framework for Object Localization
IEEE Transactions on Pattern Analysis and Machine Intelligence
Salient region detection by modeling distributions of color and orientation
IEEE Transactions on Multimedia
ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
Unsupervised extraction of visual attention objects in color images
IEEE Transactions on Circuits and Systems for Video Technology
Salient object detection: a benchmark
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part II
Scale and Object Aware Image Thumbnailing
International Journal of Computer Vision
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Accurate localization of the salient object from an image is a difficult problem when the saliency map is noisy and incomplete. A fast approach to detect salient objects from images is proposed in this paper. To well balance the size of the object and the saliency it contains, the salient object detection is first formulated with the maximum saliency density on the saliency map. To obtain the global optimal solution, a branch-and-bound search algorithm is developed to speed up the detection process. Without any prior knowledge provided, the proposed method can effectively and efficiently detect salient objects from images. Extensive results on different types of saliency maps with a public dataset of five thousand images show the advantages of our approach as compared to some state-of-the-art methods.