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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)
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)
Video retrieval can be done by ranking the samples according to their probability scores that were predicted by classifiers. It is often possible to improve the retrieval performance by re-ranking the samples. In this paper, we proposed a re-ranking method that improves the performance of semantic video indexing and retrieval, by re-evaluating the scores of(More)
We propose and evaluate in this paper a combination of Active Learning and Multiple Classifiers approaches for corpus annotation and concept indexing on highly imbalanced datasets. Experiments were conducted using TRECVID 2008 data and protocol with four different types of video shot descriptors, with two types of classifiers (Logistic Regression and(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)
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 our participation to the TRECVID 2009 High Level Features detection task. We evaluated a large number of different descriptors (on TRECVID 2008 data) and tried different fusion strategies, in particular hierarchical fusion and genetic fusion.(More)
This paper describes the LIG participation to the MediaEval 2011 Affect Task on violent scenes' detection in Hollywood movies. We submitted only the required run (shot classification run) with a minimal system using only the visual information. Color, texture and SIFT descriptors were extracted from key frames. The performance of our system was below the(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)