A Model of Saliency-Based Visual Attention for Rapid Scene Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Efficient Graph-Based Image Segmentation
International Journal of Computer Vision
The relationship between Precision-Recall and ROC curves
ICML '06 Proceedings of the 23rd international conference on Machine learning
An introduction to ROC analysis
Pattern Recognition Letters - Special issue: ROC analysis in pattern recognition
Visual attention detection in video sequences using spatiotemporal cues
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
2006 Special Issue: Modeling attention to salient proto-objects
Neural Networks
LabelMe: A Database and Web-Based Tool for Image Annotation
International Journal of Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
A simple method for detecting salient regions
Pattern Recognition
Spatiotemporal Saliency in Dynamic Scenes
IEEE Transactions on Pattern Analysis and Machine Intelligence
Esaliency (Extended Saliency): Meaningful Attention Using Stochastic Image Modeling
IEEE Transactions on Pattern Analysis and Machine Intelligence
Visual attention guided bit allocation in video compression
Image and Vision Computing
Segmenting salient objects from images and videos
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part V
Learning to Detect a Salient Object
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computational versus Psychophysical Bottom-Up Image Saliency: A Comparative Evaluation Study
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image Signature: Highlighting Sparse Salient Regions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Global salient information maximization for saliency detection
Image Communication
Visual saliency detection by spatially weighted dissimilarity
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
From co-saliency to co-segmentation: An efficient and fully unsupervised energy minimization model
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
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
Fast and Robust Generation of Feature Maps for Region-Based Visual Attention
IEEE Transactions on Image Processing
Salient Motion Features for Video Quality Assessment
IEEE Transactions on Image Processing
Multi-Task Rank Learning for Visual Saliency Estimation
IEEE Transactions on Circuits and Systems for Video Technology
Visual Attention in Objective Image Quality Assessment: Based on Eye-Tracking Data
IEEE Transactions on Circuits and Systems for Video Technology
Saliency filters: Contrast based filtering for salient region detection
CVPR '12 Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
ORB: An efficient alternative to SIFT or SURF
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
Multi-spectral saliency detection
Pattern Recognition Letters
Neurocomputing
| Hi-index | 0.01 |
This paper proposes a novel method for visual saliency detection based on an universal probabilistic model, which measures the saliency by combining low level features and location prior. We view the task of estimating visual saliency as searching the most conspicuous parts in an image and extract the saliency map by computing the dissimilarity between different regions. We simulate the moving of the center of human visual field, and describe how the center shift process works on visual saliency. Furthermore, multiscale analysis is adopted for improving the robustness of our model. Experimental results on three public image datasets show that the proposed approach outperforms 18 state-of-the-art methods for both salient object detection and human eye fixation prediction.