Charting past, present, and future research in ubiquitous computing
ACM Transactions on Computer-Human Interaction (TOCHI) - Special issue on human-computer interaction in the new millennium, Part 1
Accordion summarization for end-game browsing on PDAs and cellular phones
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Computer
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume I - Volume I
An Online Algorithm for Segmenting Time Series
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Location-Aware Information Delivery with ComMotion
HUC '00 Proceedings of the 2nd international symposium on Handheld and Ubiquitous Computing
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
The wearable remembrance agent: a system for augmented memory
ISWC '97 Proceedings of the 1st IEEE International Symposium on Wearable Computers
Visual Contextual Awareness in Wearable Computing
ISWC '98 Proceedings of the 2nd IEEE International Symposium on Wearable Computers
Learning Significant Locations and Predicting User Movement with GPS
ISWC '02 Proceedings of the 6th IEEE International Symposium on Wearable Computers
Position-Annotated Photographs: A Geotemporal Web
IEEE Pervasive Computing
Journal of Visual Communication and Image Representation
Hi-index | 0.00 |
This paper addresses a still original issue and a solution that, while emerging from the pattern recognition point of view, certainly shares common goals with mobile HCI research goals. The contribution is at the crossroads of multimedia data analysis for content-based retrieval, and wearable computing. As users are acquiring multimedia content personal mobile devices, they are getting also undergoing information overflow. The problem of structuring the content into time-oriented meaningful episodes is addressed, and we argue that geographical location processing is crucial, as a complement to processing audiovisual material. A technique for model-based temporal structuring of one's trajectory during a day is presented, based on a Bayesian/MAP approach, that generates one or several summaries. Experimental results illustrate the applicative interest of the problem addressed and validates the proposed solution.