Qualitative descriptors and action perception

Abstract : This article presents the notion of qualitative descriptor, a theoretical tool which describes within the same formalism different approaches to transform quantitative data into qualitative data. This formalism is used with a grouping algorithm to extract qualitative phases from a data flow. Work on action perception, based on qualitative descriptors, is used to illustrate these ideas. The grouping algorithm generates a qualitative symbolic data flow from a video sequence. The ultimate aim is to provide an unsupervised learning algorithm working this qualitative flow to extract abstract description for common actions such as “take”, “push” and “pull”.
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Conference papers
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https://hal.archives-ouvertes.fr/hal-01573430
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Submitted on : Wednesday, August 9, 2017 - 3:12:42 PM
Last modification on : Thursday, March 21, 2019 - 12:59:50 PM

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Jean-Christophe Baillie, Jean-Gabriel Ganascia. Qualitative descriptors and action perception. AI 2000 - 13th Biennial Conference of the Canadian Society for Computational Studies of Intelligence, May 2000, Montréal, Québec, Canada. pp.316-325, ⟨10.1007/3-540-45486-1_26⟩. ⟨hal-01573430⟩

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