Abstract
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.
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References
Bar, M.: Visual objects in context. Nature Reviews Neuroscience 5, 617–629 (2004)
Rabinovich, A., Vedaldi, C., Galleguillos, C., Wiewiora, E., Belongie, S.: Objects in context. In: Proc. of IEEE Int. Conf. on Computer Vision (2007)
Atsumi, M.: Object categorization in context based on probabilistic learning of classification tree with boosted features and co-occurrence structure. In: Bebis, G., et al. (eds.) ISVC 2013, Part I. LNCS, vol. 8033, pp. 416–426. Springer, Heidelberg (2013)
Isahara, H., Bond, F., Uchimoto, K., Utiyama, M., Kanzaki, K.: Development of Japanese WordNet. In: Proc. of the 6th Int. Conf. on Language Resources and Evaluation, pp. 2420–2423 (2008)
Atsumi, M.: Learning visual categories based on probabilistic latent component models with semi-supervised labeling. GSTF Int. Journal on Computing 2(1), 88–93 (2012)
Kitani, K., Okabe, T., Sato, Y.: Discovering primitive action categories by leveraging relevant visual context. In: Proc. of the IEEE Int. WS on Visual Surveillance (2008)
Yao, B., Fei-Fei, L.: Recognizing human-object interactions in still images by modeling the mutual context of objects and human poses. IEEE Trans. on Pattern Analysis and Machine Intelligence 34(9), 1691–1703 (2012)
Yao, B., Ma, J., Fei-Fei, L.: Discovering object functionality. In: Proc. of Int. Conf. on Computer Vision (2013)
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Atsumi, M. (2014). Learning Probabilistic Semantic Network of Object-Oriented Action and Activity. In: Agre, G., Hitzler, P., Krisnadhi, A.A., Kuznetsov, S.O. (eds) Artificial Intelligence: Methodology, Systems, and Applications. AIMSA 2014. Lecture Notes in Computer Science(), vol 8722. Springer, Cham. https://doi.org/10.1007/978-3-319-10554-3_1
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DOI: https://doi.org/10.1007/978-3-319-10554-3_1
Publisher Name: Springer, Cham
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Online ISBN: 978-3-319-10554-3
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