Temporal reasoning based on semi-intervals
Artificial Intelligence
Machine Learning
Maintaining knowledge about temporal intervals
Communications of the ACM
MacVisSTA: a system for multimodal analysis
Proceedings of the 6th international conference on Multimodal interfaces
Discovering Frequent Episodes and Learning Hidden Markov Models: A Formal Connection
IEEE Transactions on Knowledge and Data Engineering
Toward a Common Event Model for Multimedia Applications
IEEE MultiMedia
The catchment feature model: a device for multimodal fusion and a bridge between signal and sense
EURASIP Journal on Applied Signal Processing
Unsupervised pattern mining from symbolic temporal data
ACM SIGKDD Explorations Newsletter - Special issue on data mining for health informatics
VCode and VData: illustrating a new framework for supporting the video annotation workflow
AVI '08 Proceedings of the working conference on Advanced visual interfaces
Creating Rapport with Virtual Agents
IVA '07 Proceedings of the 7th international conference on Intelligent Virtual Agents
ICMI '08 Proceedings of the 10th international conference on Multimodal interfaces
An exchange format for multimodal annotations
Multimodal corpora
International Conference on Multimodal Interfaces and the Workshop on Machine Learning for Multimodal Interaction
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Toward multimodal situated analysis
ICMI '11 Proceedings of the 13th international conference on multimodal interfaces
MLMI'05 Proceedings of the Second international conference on Machine Learning for Multimodal Interaction
The AMI meeting corpus: a pre-announcement
MLMI'05 Proceedings of the Second international conference on Machine Learning for Multimodal Interaction
VACE multimodal meeting corpus
MLMI'05 Proceedings of the Second international conference on Machine Learning for Multimodal Interaction
A multimodal analysis of floor control in meetings
MLMI'06 Proceedings of the Third international conference on Machine Learning for Multimodal Interaction
Interactive data-driven discovery of temporal behavior models from events in media streams
Proceedings of the 20th ACM international conference on Multimedia
Interactive data-driven search and discovery of temporal behavior patterns from media streams
Proceedings of the 20th ACM international conference on Multimedia
Interactive relevance search and modeling: support for expert-driven analysis of multimodal data
Proceedings of the 15th ACM on International conference on multimodal interaction
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There are a multitude of annotated behavior corpora (manual and automatic annotations) available as research expands in multimodal analysis of human behavior. Despite the rich representations within these datasets, search strategies are limited with respect to the advanced representations and complex structures describing human interaction sequences. The relationships amongst human interactions are structural in nature. Hence, we present Structural and Temporal Inference Search (STIS) to support search for relevant patterns within a multimodal corpus based on the structural and temporal nature of human interactions. The user defines the structure of a behavior of interest driving a search focused on the characteristics of the structure. Occurrences of the structure are returned. We compare against two pattern mining algorithms purposed for pattern identification amongst sequences of symbolic data (e.g., sequence of events such as behavior interactions). The results are promising as STIS performs well with several datasets.