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
Image-Segmentation Evaluation From the Perspective of Salient Object Extraction
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
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
A simple method for detecting salient regions
Pattern Recognition
Benchmarking Image Segmentation Algorithms
International Journal of Computer Vision
A dataset and evaluation methodology for visual saliency in video
ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
Salient region detection and segmentation
ICVS'08 Proceedings of the 6th international conference on Computer vision systems
Saliency detection for content-aware image resizing
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
IEEE Transactions on Image Processing
Probabilistic Multi-Task Learning for Visual Saliency Estimation in Video
International Journal of Computer Vision
Object of interest detection by saliency learning
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part II
Segmenting salient objects from images and videos
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part V
Saliency density maximization for object detection and localization
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part III
IEEE Transactions on Pattern Analysis and Machine Intelligence
A generic framework of user attention model and its application in video summarization
IEEE Transactions on Multimedia
An Adaptive Computational Model for Salient Object Detection
IEEE Transactions on Multimedia
Automatic foveation for video compression using a neurobiological model of visual attention
IEEE Transactions on Image Processing
A Co-Saliency Model of Image Pairs
IEEE Transactions on Image Processing
Understanding and predicting where people look in images
Understanding and predicting where people look in images
Center-surround divergence of feature statistics for salient object detection
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
Optimal contrast based saliency detection
Pattern Recognition Letters
Salient object detection in videos by optimal spatio-temporal path discovery
Proceedings of the 21st ACM international conference on Multimedia
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Several salient object detection approaches have been published which have been assessed using different evaluation scores and datasets resulting in discrepancy in model comparison. This calls for a methodological framework to compare existing models and evaluate their pros and cons. We analyze benchmark datasets and scoring techniques and, for the first time, provide a quantitative comparison of 35 state-of-the-art saliency detection models. We find that some models perform consistently better than the others. Saliency models that intend to predict eye fixations perform lower on segmentation datasets compared to salient object detection algorithms. Further, we propose combined models which show that integration of the few best models outperforms all models over other datasets. By analyzing the consistency among the best models and among humans for each scene, we identify the scenes where models or humans fail to detect the most salient object. We highlight the current issues and propose future research directions.