A Model of Saliency-Based Visual Attention for Rapid Scene Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fast Approximate Energy Minimization via Graph Cuts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Motion detection with nonstationary background
Machine Vision and Applications
Background Modeling and Subtraction of Dynamic Scenes
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Contrast-based image attention analysis by using fuzzy growing
MULTIMEDIA '03 Proceedings of the eleventh ACM international conference on Multimedia
What Energy Functions Can Be Minimizedvia Graph Cuts?
IEEE Transactions on Pattern Analysis and Machine Intelligence
"GrabCut": interactive foreground extraction using iterated graph cuts
ACM SIGGRAPH 2004 Papers
Histograms of Oriented Gradients for Human Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Bayesian Modeling of Dynamic Scenes for Object Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Texture-Based Method for Modeling the Background and Detecting Moving Objects
IEEE Transactions on Pattern Analysis and Machine Intelligence
Bilayer Segmentation of Live Video
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Seam carving for content-aware image resizing
ACM SIGGRAPH 2007 papers
Learning CRFs Using Graph Cuts
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part II
Efficient Subwindow Search: A Branch and Bound Framework for Object Localization
IEEE Transactions on Pattern Analysis and Machine Intelligence
Spatiotemporal Saliency in Dynamic Scenes
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
Salient region detection using weighted feature maps based on the human visual attention model
PCM'04 Proceedings of the 5th Pacific Rim Conference on Advances in Multimedia Information Processing - Volume Part II
Unsupervised extraction of visual attention objects in color images
IEEE Transactions on Circuits and Systems for Video Technology
Real-time detection of small surface objects using weather effects
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part III
Fast and efficient saliency detection using sparse sampling and kernel density estimation
SCIA'11 Proceedings of the 17th Scandinavian conference on Image analysis
Video object segmentation with shortest path
Proceedings of the 20th ACM international conference on Multimedia
TriCoS: a tri-level class-discriminative co-segmentation method for image classification
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part I
Salient object detection: a benchmark
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part II
Visual saliency detection with center shift
Neurocomputing
Two-layer average-to-peak ratio based saliency detection
Image Communication
Temporal saliency for fast motion detection
ACCV'12 Proceedings of the 11th international conference on Computer Vision - Volume Part I
An edge detection with automatic scale selection approach to improve coherent visual attention model
Pattern Recognition Letters
Stochastic bottom-up fixation prediction and saccade generation
Image and Vision Computing
Mesh saliency via spectral processing
ACM Transactions on Graphics (TOG)
Tag-Saliency: Combining bottom-up and top-down information for saliency detection
Computer Vision and Image Understanding
SalientShape: group saliency in image collections
The Visual Computer: International Journal of Computer Graphics
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In this paper we introduce a new salient object segmentation method, which is based on combining a saliency measure with a conditional random field (CRF) model. The proposed saliency measure is formulated using a statistical framework and local feature contrast in illumination, color, and motion information. The resulting saliency map is then used in a CRF model to define an energy minimization based segmentation approach, which aims to recover well-defined salient objects. The method is efficiently implemented by using the integral histogram approach and graph cut solvers. Compared to previous approaches the introduced method is among the few which are applicable to both still images and videos including motion cues. The experiments show that our approach outperforms the current state-of-the-art methods in both qualitative and quantitative terms.