A Model of Saliency-Based Visual Attention for Rapid Scene Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Detecting Salient Motion by Accumulating Directionally-Consistent Flow
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Trainable System for Object Detection
International Journal of Computer Vision - special issue on learning and vision at the center for biological and computational learning, Massachusetts Institute of Technology
Robust motion estimation using spatial Gabor-like filters
Signal Processing
Non-parametric Model for Background Subtraction
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part II
Semantics-Based Image Retrieval by Region Saliency
CIVR '02 Proceedings of the International Conference on Image and Video Retrieval
Improved Adaptive Gaussian Mixture Model for Background Subtraction
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 2 - Volume 02
A Principled Approach to Detecting Surprising Events in Video
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
A Texture-Based Method for Modeling the Background and Detecting Moving Objects
IEEE Transactions on Pattern Analysis and Machine Intelligence
Visual attention detection in video sequences using spatiotemporal cues
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
Seam carving for content-aware image resizing
ACM SIGGRAPH 2007 papers
Improved seam carving for video retargeting
ACM SIGGRAPH 2008 papers
Photo and Video Quality Evaluation: Focusing on the Subject
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part III
Modelling Spatio-Temporal Saliency to Predict Gaze Direction for Short Videos
International Journal of Computer Vision
An iterative image registration technique with an application to stereo vision
IJCAI'81 Proceedings of the 7th international joint conference on Artificial intelligence - Volume 2
Context saliency based image summarization
ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
Image pre-classification based on saliency map for image retrieval
ICICS'09 Proceedings of the 7th international conference on Information, communications and signal processing
Sustained observability for salient motion detection
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part III
Image segmentation in video sequences: a probabilistic approach
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
Global contrast based salient region detection
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
ViBe: A Universal Background Subtraction Algorithm for Video Sequences
IEEE Transactions on Image Processing
Background subtraction via coherent trajectory decomposition
Proceedings of the 21st ACM international conference on Multimedia
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Video saliency detection, the task to detect attractive content in a video, has broad applications in multimedia understanding and retrieval. In this paper, we propose a new framework for spatiotemporal saliency detection. To better estimate the salient motion in temporal domain, we take advantage of robust alignment by sparse and low-rank decomposition to jointly estimate the salient foreground motion and the camera motion. Consecutive frames are transformed and aligned, and then decomposed to a low-rank matrix representing the background and a sparse matrix indicating the objects with salient motion. In the spatial domain, we address several problems of local center-surround contrast based model, and demonstrate how to utilize global information and prior knowledge to improve spatial saliency detection. Individual component evaluation demonstrates the effectiveness of our temporal and spatial methods. Final experimental results show that the combination of our spatial and temporal saliency maps achieve the best overall performance compared to several state-of-the-art methods.