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 Im-ageCLEF 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)
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)
Regim_4: The indexing process is based on the visual modality analysis and relationships within LSCOM Ontology to improve the detection of large set of semantic concepts. The visual modality analysis is orientated towards an automatic categorization of video contents to create relevance relationships between low-level descriptions and semantic contents(More)
Providing a semantic access to video data requires the development of concept detectors. However, semantic concepts detection is a hard task due to the large intra-class and the small inter-class variability of content. Moreover, semantic concepts co-occur together in various contexts and their occurrence may vary from one to another. Thus, it is(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)