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Erschienen in:
2014 | OriginalPaper | Buchkapitel
Learning Probabilistic Semantic Network of Object-Oriented Action and Activity
verfasst von : Masayasu Atsumi
Erschienen in: Artificial Intelligence: Methodology, Systems, and Applications
Verlag: Springer International Publishing
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This paper proposes a method of learning probabilistic semantic networks which represent visual features and semantic features of object-oriented actions and their contextual activities. In this method, visual motion feature classes of actions and activities are learned by an unsupervised Incremental Probabilistic Latent Component Analysis (IPLCA) and these classes and their semantic tags in the form of case triplets are integrated into probabilistic semantic networks to visually recognize and verbally infer actions in the context of activities. Through experiments using video clips captured with the Kinect sensor, it is shown that the method can learn, recognize and infer object-oriented actions in the context of activities.