Learning the distribution of object trajectories for event recognition
BMVC '95 Proceedings of the 6th British conference on Machine vision (Vol. 2)
LOF: identifying density-based local outliers
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Learning Patterns of Activity Using Real-Time Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
Video summarisation: A conceptual framework and survey of the state of the art
Journal of Visual Communication and Image Representation
The Pascal Visual Object Classes (VOC) Challenge
International Journal of Computer Vision
Affective image classification using features inspired by psychology and art theory
Proceedings of the international conference on Multimedia
Detecting unusual activity in video
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Coherence progress: a measure of interestingness based on fixed compressors
AGI'11 Proceedings of the 4th international conference on Artificial general intelligence
What makes an image memorable?
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
High level describable attributes for predicting aesthetics and interestingness
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Online domain adaptation of a pre-trained cascade of classifiers
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Beyond Novelty Detection: Incongruent Events, When General and Specific Classifiers Disagree
IEEE Transactions on Pattern Analysis and Machine Intelligence
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Interestingness is said to be the power of attracting or holding one's attention (because something is unusual or exciting, etc.). We, as humans, have the great capacity to direct our visual attention and judge the interestingness of a scene. Consider for example the image sequence in the figure on the right. The spider in front of the camera or the snow on the lens are examples of events that deviate from the context since they violate the expectations, and therefore are considered interesting. On the other hand, weather changes or a camera shift, do not raise human attention considerably, even though large regions of the image are influenced. In this work we firstly investigate what humans consider as "interesting" in image sequences. Secondly we propose a computer vision algorithm to automatically spot these interesting events. To this end, we integrate multiple cues inspired by cognitive concepts and discuss why and to what extent the automatic discovery of visual interestingness is possible.