Bahjat Safadi

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This paper describes the LIG participation to the MediaEval 2013 Affect Task on violent scenes detection in Hollywood movies. We submitted four runs at the shot level for each subtasks: objective violent scenes detection and subjective violent scenes detection. Our four runs are: hierarchical fusion of descriptors and classifier combinations, the same with(More)
Currently, popular search engines retrieve documents on the basis of text information. However, integrating the visual information with the text-based search for video and image retrieval is still a hot research topic. In this paper, we propose and evaluate a video search framework based on using visual information to enrich the classic text-based search(More)
The IRIM group is a consortium of French teams working on Multimedia Indexing and Retrieval. This paper describes its participation to the TRECVID 2011 semantic indexing and instance search tasks. For the semantic indexing task, our approach uses a six-stages processing pipelines for computing scores for the likelihood of a video shot to contain a target(More)
This paper describes the LIG participation to the MediaEval 2012 Affect Task on violent scenes’ detection in Hollywood movies. We submitted four runs at the shot level: hierarchical fusion of descriptors and classifier combinations (LIG-4), the same with conceptual feedback (LIG-3), and the same two with reranking (LIG-2 and LIG-1). Our reference run(More)
This paper presents a set of improvements for SVMbased large scale multimedia indexing. The proposed method is particularly suited for the detection of many target concepts at once and for highly imbalanced classes (very infrequent concepts). The method is based on the use of multiple SVMs (MSVM) for dealing with the class imbalance and on some adaptations(More)
In this working notes paper the contribution of the LIG team (partnership between Univ. Grenoble Alpes and Ozyegin University) to the Multimodal Person Discovery in Broadcast TV task in MediaEval 2015 is presented. The task focused on unsupervised learning techniques. Two different approaches were submitted by the team. In the first one, new features for(More)
We describe in this paper the different approaches tested for the Photo Annotation task for CLEF 2011. We experimented state of the art techniques, by proposing late fusions of several classifiers trained on several features extracted from the images. The classifiers are SVMs and the late fusion is a simple addition of classification probabilities coming(More)
In this paper, we propose and evaluate a method for optimizing descriptors used for content-based multimedia indexing and retrieval. A large variety of descriptors are commonly used for this purpose. However, the most efficient ones often have characteristics preventing them to be easily used in large scale systems. They may have very high dimensionality(More)