Maguelonne Héritier

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An improved unsupervised classification method to extract and link places features and cluster recurrent physical locations (key-places) within a movie is presented. Our approach finds links between key frames of a common key-place based on the use of a probabilistic latent space model over the possible local matches between the key frames image set. This(More)
Approach we have tested in our submitted runs: For visualbased copy detection, we find links between video shot key-frames using a probabilistic latent space model over local matches between the keyframe images. This facilitates the extraction of significant groups of local matching descriptors that may represent common semantic elements of near duplicate(More)
We describe a new method to find and cluster recurrent key-places in a movie. It consists of an unsupervised classification of shots that are taking place in the same physical location (key-place). Our approach is based on finding links between key-frames belonging to a same key-place. We use a probabilistic latent space model over the possible match points(More)
We present an application of video indexing/summarization to produce Videodescription (VD) for the blinds. Audio and computer vision technologies can automatically detect and recognize many elements that are pertinent to VD which can speed-up the VD production process. We have developed and integrated many of them into a first computer-assisted VD(More)
This paper presents the status of a R&D project targeting the development of computer-vision tools to assist humans in generating and rendering video description for people with vision loss. Three principal issues are discussed: (1) production practices, (2) needs of people with vision loss, and (3) current system design, core technologies and(More)
Approach we have tested in our submitted runs. Our approach consists in finding links between video shot key-frames, based on the use of a probabilistic latent space model over local matches between the keyframe images. This allows the extraction of significant groups of local matching descriptors that may represent common characteristic elements of near(More)
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