SIGGRAPH '96 Proceedings of the 23rd annual conference on Computer graphics and interactive techniques
ComicDiary: Representing Individual Experiences in a Comics Style
UbiComp '02 Proceedings of the 4th international conference on Ubiquitous Computing
Teaching Machines about Everyday Life
BT Technology Journal
ContextPhone: A Prototyping Platform for Context-Aware Mobile Applications
IEEE Pervasive Computing
Bayesphone: precomputation of context-sensitive policies for inquiry and action in mobile devices
UM'05 Proceedings of the 10th international conference on User Modeling
Automatic generation of funny cartoons diary for everyday mobile life
AI'06 Proceedings of the 19th Australian joint conference on Artificial Intelligence: advances in Artificial Intelligence
Modular bayesian networks for inferring landmarks on mobile daily life
AI'06 Proceedings of the 19th Australian joint conference on Artificial Intelligence: advances in Artificial Intelligence
Frame Selection for Automatic Comic Generation from Game Log
ICEC '08 Proceedings of the 7th International Conference on Entertainment Computing
Rule-based camerawork controller for automatic comic generation from game log
ICEC'10 Proceedings of the 9th international conference on Entertainment computing
Camerawork for comics generated from visitors' experiences in a virtual museum
ICEC'11 Proceedings of the 10th international conference on Entertainment Computing
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Mobile devices are treasure boxes of personal information containing user's context, personal schedule, diary, short messages, photos, and videos. Also, user's usage information on Smartphone can be recorded on the device and they can be used as useful sources of high-level inference. Furthermore, stored multimedia contents can be also regarded as relevant evidences for inferring user's daily life. Without user's consciousness, the device continuously collects information and it can be used as an extended memory of human users. However, the amount of information collected is extremely huge and it is difficult to extract useful information manually from the raw data. In this paper, AniDiary (Anywhere Diary) is proposed to summarize user's daily life in a form of cartoonstyle diary. Because it is not efficient to show all events in a day, selected landmark events (memorable events) are automatically converted to the cartoon images. The identification of landmark events is done by modeling causal-effect relationships among various events with a number of Bayesian networks. Experimental results on synthetic data showed that the proposed system provides an efficient and user-friendly way to summarize user's daily life.