Anis Ben Ammar

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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)
A video retrieval system user hopes to find relevant information when the proposed queries are ambiguous. The retrieval process based on detecting concepts remains ineffective in such a situation. Potential relationships between concepts have been shown as a valuable knowledge resource that can enhance the retrieval effectiveness, even for ambiguous(More)
In this paper, we describe our participation in the Image-CLEF 2015 Scalable Concept Image Annotation task. In this participation , we display our approach for an automatic image annotation by the use of an ontology-based semantic hierarchy handled at both learning and annotation steps. While recent works focused on the use of semantic hierarchies to(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)