Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Learning the distribution of object trajectories for event recognition
BMVC '95 Proceedings of the 6th British conference on Machine vision (Vol. 2)
An on-line agglomerative clustering method for nonstationary data
Neural Computation
Learning Patterns of Activity Using Real-Time Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Bayesian Computer Vision System for Modeling Human Interactions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Video Google: A Text Retrieval Approach to Object Matching in Videos
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Histograms of Oriented Gradients for Human Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Photo tourism: exploring photo collections in 3D
ACM SIGGRAPH 2006 Papers
A System for Learning Statistical Motion Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
Web-based 3D Reconstruction Service
Machine Vision and Applications
Robust Real-Time Unusual Event Detection using Multiple Fixed-Location Monitors
IEEE Transactions on Pattern Analysis and Machine Intelligence
World-scale mining of objects and events from community photo collections
CIVR '08 Proceedings of the 2008 international conference on Content-based image and video retrieval
What Does the Sky Tell Us about the Camera?
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part IV
Scene Segmentation for Behaviour Correlation
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part IV
Detecting unusual activity in video
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Learning semantic scene models from observing activity in visual surveillance
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Real-time detection of unusual regions in image streams
Proceedings of the international conference on Multimedia
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part I
Multimedia data mining: state of the art and challenges
Multimedia Tools and Applications
The InfoAlbum image centric information collection
Proceedings of the International Conference on Web Intelligence, Mining and Semantics
Robust Duplicate Detection of 2D and 3D Objects
International Journal of Multimedia Data Engineering & Management
| Hi-index | 0.00 |
So far, most image mining was based on interactive querying. Although such querying will remain important in the future, several applications need image mining at such wide scales that it has to run automatically. This adds an additional level to the problem, namely to apply appropriate further processing to different types of images, and to decide on such processing automatically as well. This paper touches on those issues in that we discuss the processing of landmark images and of images coming from webcams. The first part deals with the automated collection of images of landmarks, which are then also automatically annotated and enriched with Wikipedia information. The target application is that users photograph landmarks with their mobile phones or PDAs, and automatically get information about them. Similarly, users can get images in their photo albums annotated automatically. The object of interest can also be automatically delineated in the images. The pipeline we propose actually retrieves more images than manual keyword input would produce. The second part of the paper deals with an entirely different source of image data, but one that also produces massive amounts (although typically not archived): webcams. They produce images at a single location, but rather continuously and over extended periods of time. We propose an approach to summarize data coming from webcams. This data handling is quite different from that applied to the landmark images.