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
Improved seam carving for video retargeting
ACM SIGGRAPH 2008 papers
Online Learning for Matrix Factorization and Sparse Coding
The Journal of Machine Learning Research
Incremental sparse saliency detection
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
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
Bottom-up saliency based on weighted sparse coding residual
MM '11 Proceedings of the 19th ACM international conference on Multimedia
A biologically inspired computational model for image saliency detection
MM '11 Proceedings of the 19th ACM international conference on Multimedia
Image Signature: Highlighting Sparse Salient Regions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Global contrast based salient region detection
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Foveation scalable video coding with automatic fixation selection
IEEE Transactions on Image Processing
Unsupervised extraction of visual attention objects in color images
IEEE Transactions on Circuits and Systems for Video Technology
Exploiting local and global patch rarities for saliency detection
CVPR '12 Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Salient object detection: a benchmark
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part II
State-of-the-Art in Visual Attention Modeling
IEEE Transactions on Pattern Analysis and Machine Intelligence
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Saliency detection has been gaining increasing attention in recent years since it could significantly boost many content-based multimedia applications. Most traditional approaches adopt the predefined local contrast, global contrast, or heuristic combination of them to measure saliency. In this paper, based on the underlying premises that human visual attention mechanisms work adaptively for various scales and salient objects can maximally pop out with respect to the background within a specific surrounding area, we propose a novel saliency detection method using a new concept of optimal contrast. A number of contrast hypotheses are first calculated with various surrounding areas by means of sparse coding principles. Afterwards, these hypotheses are compared using an entropy-based criterion and the optimal contrast is selected which is treated as the core factor for building the saliency map. Finally, a multi-scale enhancement is performed to further refine the results. Comprehensive evaluations on three publicly available benchmark datasets and comparisons with many up-to-date algorithms demonstrate the effectiveness of the proposed work.