Anis Ben Ammar

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Multimedia indexing systems based on semantic concept detectors are incomplete in the semantic sense. We can improve the effectiveness of these systems by using knowledge-based approaches which utilize semantic knowledge. In this paper, we propose a novel and efficient approach to enhance semantic concept detection in multimedia content, by exploiting(More)
This paper describes the participation of the REGIM team in the ImageCLEF 2013 Robot Vision Challenge. The competition was focused on the problem of objects and scenes classification in indoor environments. Objects and scenes are considered as concepts. During the competition, we aim to classify images according to the room in which they were acquired,(More)
In this paper, we describe an overview of a software platform that has been developed within REGIMVid project for TRECVID 2010 video retrieval experiments. The REGIMVID team participated in Semantic Indexing task. In TRECVID 2010, we explore several novel techniques to perform the detection of semantic concepts, including multi classifiers with supervised(More)
This paper handles with two main challenges: retrieving the best matching images to a given query and improving diversity in ranking using fuzzy logic. The proposed scheme proceeds as follows: First, an off line module is performed before starting the image retrieval process in order to reduce both, the execution time and the algorithm complexity. This(More)
The paper proposes a novel semi-automatic soft collaborative annotation scheme for video semantic indexing. To annotate video data effectively and accurately, a video collaborative soft annotation within users' judgment modeling is first proposed in this paper. We, then, introduce a semiautomatic annotation strategy which combines the active learning and(More)