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The Transferable Belief Model (TBM) relies on belief functions and enables one to represent and combine a variety of knowledge from certain up to ignorance as well as conflict inherent to imperfect data. A lot of applications have used this flexible framework however, in the context of temporal data analysis of belief functions, a few work have been(More)
The IRIM group is a consortium of French teams working on Multimedia Indexing and Retrieval. This paper describes our participation to the TRECVID 2010 semantic indexing and instance search tasks. For the semantic indexing task, we evaluated a number of different descriptors and tried different fusion strategies, in particular hierarchical fusion. The best(More)
An automatic human shape-motion analysis method based on a fusion architecture is proposed for human action recognition in videos. Robust shape-motion features are extracted from human points detection and tracking. The features are combined within the Transfer-able Belief Model (TBM) framework for action recognition. The TBM-based modelling and fusion(More)
This paper presents a system for classifying facial expressions based on a data fusion process relying on the Belief Theory (BeT). Four expressions are considered: joy, surprise, disgust as well as neutral. The proposed system is able to take into account intrinsic doubt about emotion in the recognition process and to handle the fact that each person has(More)
In the context of human action recognition in video sequences, a temporal belief filter based on the Transferable Belief Model is proposed. It ensures a consistency in the temporal belief evolution. The filter is useful to cope with varying video quality and experiment conditions by smoothing belief on actions and solving conflict due to contradictory(More)
Many applications are concerned by human action recognition notably in multimedia and more particularly for video retrieval and archival. Usual approaches focus on proba-bilistic methods and assume a still camera. In this paper , a method based on the Transferable Belief Model fusion process and considering a moving camera is proposed. In this framework,(More)
A method for the classification of facial expressions from the analysis of facial deformations is presented. Neutral. The proposed classifier relies on data coming from a contour segmentation technique, which extracts an expression skeleton of facial features (mouth, eyes and eyebrows) and derives simple distance coefficients from every face image of a(More)
This paper focuses on human behavior recognition where the main problem is to bridge the semantic gap between the analogue observations of the real world and the symbolic world of human interpretation. For that, a fusion architecture based on the Transfer-able Belief Model framework is proposed and applied to action recognition of an athlete in video(More)