The robust estimation of multiple motions: parametric and piecewise-smooth flow fields
Computer Vision and Image Understanding
A Model of Saliency-Based Visual Attention for Rapid Scene Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope
International Journal of Computer Vision
Statistical Background Subtraction for a Mobile Observer
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Epitomic analysis of appearance and shape
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Automatic thumbnail cropping and its effectiveness
Proceedings of the 16th annual ACM symposium on User interface software and technology
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Robust Object Detection via Soft Cascade
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
A Flow-Based Approach to Vehicle Detection and Background Mosaicking in Airborne Video
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Automatic image retargeting with fisheye-view warping
Proceedings of the 18th annual ACM symposium on User interface software and technology
ACM SIGGRAPH 2006 Papers
Detecting Irregularities in Images and in Video
International Journal of Computer Vision
LIBLINEAR: A Library for Large Linear Classification
The Journal of Machine Learning Research
PatchMatch: a randomized correspondence algorithm for structural image editing
ACM SIGGRAPH 2009 papers
Image thumbnails that represent blur and noise
IEEE Transactions on Image Processing
Image change detection algorithms: a systematic survey
IEEE Transactions on Image Processing
| Hi-index | 0.00 |
Image triage is a common task in digital photography. Determining which photos are worth processing for sharing with friends and family and which should be deleted to make room for new ones can be a challenge, especially on a device with a small screen like a mobile phone or camera. In this work we explore the importance of local structure changes?e.g. human pose, appearance changes, object orientation, etc.?to the photographic triage task. We perform a user study in which subjects are asked to mark regions of image pairs most useful in making triage decisions. From this data, we train a model for image saliency in the context of other images that we call cosaliency. This allows us to create collection-aware crops that can augment the information provided by existing thumbnailing techniques for the image triage task.