NeuralStory: an Interactive Multimedia System for Video Indexing and Re-use
Abstract: In the last years video has been swamping the Internet: websites, social networks, and business multimedia systems are adopting video as the most important form of communication and information. Video are normally accessed as a whole and are not indexed in the visual content. Thus, they are often uploaded as short, manually cut clips with user-provided annotations, keywords and tags for retrieval. In this paper, we propose a prototype multimedia system which addresses these two limitations: it overcomes the need of human intervention in the video setting, thanks to fully deep learning-based solutions, and decomposes the storytelling structure of the video into coherent parts. These parts can be shots, key-frames, scenes and semantically related stories, and are exploited to provide an automatic annotation of the visual content, so that parts of video can be easily retrieved. This also allows a principled re-use of the video itself: users of the platform can indeed produce new storytelling by means of multi-modal presentations, add text and other media, and propose a different visual organization of the content. We present the overall solution, and some experiments on the re-use capability of our platform in edutainment by conducting an extensive user valuation %with students from primary schools.
Citation:
Baraldi, Lorenzo; Grana, Costantino; Cucchiara, Rita "NeuralStory: an Interactive Multimedia System for Video Indexing and Re-use" Proceedings of the 15th International Workshop on Content-Based Multimedia Indexing, Florence, Italy, 19-21 June 2017, 2017 DOI: 10.1145/3095713.3095735
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- DOI: 10.1145/3095713.3095735