Sabin Tiberius Strat

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We deal with the issue of combining dozens of classifiers into a better one, for concept detection in videos. We compare three fusion approaches that share a common structure: they all start with a classifier clustering stage, continue with an intra-cluster fusion and end with an inter-cluster fusion. The main difference between them comes from the first(More)
This paper investigates how the detection of diverse high-level semantic concepts (objects, actions, scene types, persons etc.) in videos can be improved by applying a model of the human retina. A large part of the current approaches for Content-Based Image/Video Retrieval (CBIR/CBVR) relies on the Bag-of-Words (BoW) model, which has shown to perform well(More)
This paper addresses the task of detecting diverse semantic concepts in videos. Within this context, the Bag Of Visual Words (BoW) model, inherited from sampled video keyframes analysis, is among the most popular methods. However, in the case of image sequences, this model faces new difficulties such as the added motion information, the extra computational(More)
This paper proposes to investigate the potential benefit of the use of low-level human vision behaviors in the context of high-level semantic concept detection. A large part of the current approaches relies on the Bag-of-Words (BoW) model, which has proven itself to be a good choice especially for object recognition in images. Its extension from static(More)
A semantic indexing system capable of detecting both spatial appearance and motion-related semantic concepts requires the use of both spatial and motion descriptors. However, extracting motion descriptors on very large video collections requires great computational resources, which has caused most approaches to limit themselves to a spatial description.(More)
This paper proposes to investigate the potential benefit of the use of low-level human vision behaviors in the context of high-level semantic concept detection. A large part of the current approaches relies on the Bag-of-Words (BoW) model, which has proven itself to be a good choice especially for object recognition in images. Its extension from static(More)
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