Automatic categorization and summarization of documentaries

In this paper, we propose automatic categorization and summarization of documentaries using subtitles of videos. We propose two methods for video categorization. The first makes unsupervised categorization by applying natural language processing techniques on video subtitles and uses the WordNet lexical database and WordNet domains. The second has the same extraction steps but uses a learning module to categorize. Experiments with documentary videos give promising results in discovering the correct categories of videos. We also propose a video summarization method using the subtitles of videos and text summarization techniques. Significant sentences in the subtitles of a video are identified using these techniques and a video summary is then composed by finding the video parts corresponding to these summary sentences. © 2010 The Author(s)

Descoberta automática de temas utilizando legendas

Tese de mestrado em Engenharia Informática, apresentada à Universidade de Lisboa, através da Faculdade de Ciências, 2012Este trabalho insere-se no projecto VIRUS (Video Information Retrieval Using Subtitles). O projecto VIRUS tem como objectivo o desenvolvimento de um sistema de Recuperação de Informações Vídeo que ira funcionar em bibliotecas de vídeos compostas por documentos legendados. Contrastando com projectos anteriores, limitamo-nos a processar filmes e series de televisão para as quais as legendas estão disponíveis. Aspectos diferenciais deste projecto incluem a recuperação de informação com base na análise simultânea de três fluxos de informação: sinal de vídeo, legendas e sinal de áudio. O sistema permite visualizar vídeos de forma significativa e aceita consultas do utilizador para encontrar partes dos documentos de vídeo as quais correspondem as consultas. Os domínios de aplicação de um sistema como este são vastos. Pode ser usado por profissionais da indústria cinematográfica para aceder e visualizar cenas que partilham algumas características, ou para produzir uma descrição concisa e detalhada de um filme, o que poderia ser um valioso contributo para um sistema de recomendação. Outro domínio de aplicação deste sistema e o anúncio contextual. A análise semântica das cenas fornece uma poderosa ferramenta para colocar anúncios relacionados nos documentos de vídeo. O trabalho “DESCOBERTA AUTOMATICA DE TEMAS UTILIZANDO LEGENDAS” explora um dos fluxos de informação que se pretende abordar no projecto VIRUS, as legendas. O seu objectivo e desenvolver algoritmos capazes de descobrir automaticamente o tema de uma conversa e sugerir quais os temas mais relevantes. Alem desse objectivo principal, há outras particularidades das legendas que podem ser analisadas e que diferenciam as series de TV. Os textos usados foram legendas de series como o 24, Anatomia de Grey, Os Sopranos, e muitas outras. O trabalho foi desenvolvido em Java e os resultados que obtemos são apresentados na interface web do MovieClouds, o protótipo do projecto VIRUS. Apesar do projecto ainda não estar terminado, concluímos, através de testes com utilizadores que o processamento das legendas são um excelente contributo para identificar temas nos vídeos.This work is inserted in the VIRUS (Video Information Retrieval Using Subtitles) project. The VIRUS project has as objective the development of a Video Information Retrieval system that will operate on video libraries composed of subtitled documents. Contrasting with previous projects, we restrict ourselves to movies and television series for which subtitles are available. Distinguishing aspects of this project include information retrieval based on the simultaneous analysis of three information streams: video signal, subtitles and audio signal. The system allows visualizing videos in meaningful ways and accepts queries from the user to find portions of video documents that match the queries. The domains of application of such a system are vast. It can be used by movie industry professionals to access and visualize scenes that share some characteristic, or to produce a concise and detailed description of a movie that could be a valuable input for a recommendation system. Another domain of application of this system is contextual advertisement. The semantic analysis of scenes provides a powerful tool for placing related advertisements on video documents. The work “DESCOBERTA AUTOMÁTICA DE TEMAS UTILIZANDO LEGENDAS” exploits one of the information fluxes that we want to approach on the VIRUS project, the subtitles. The purpose of this work is to develop algorithms that would be able to automatically identify the theme of a conversation and suggest the most relevant ones. Besides this main objective, there are other particularities of the subtitles that can be observed and that differentiate the TV series. The texts we have used are subtitles from series as 24, Grey's Anatomy, The Sopranos and many others. The work has been developed in Java and the results we obtained are shown in the web interface of MovieClouds, the prototype of the VIRUS project. The results are still preliminary, however we can conclude, by testing with users, that subtitles processing is an excellent contribution to identify themes in videos

Safety and Reliability - Safe Societies in a Changing World

The contributions cover a wide range of methodologies and application areas for safety and reliability that contribute to safe societies in a changing world. These methodologies and applications include: - foundations of risk and reliability assessment and management - mathematical methods in reliability and safety - risk assessment - risk management - system reliability - uncertainty analysis - digitalization and big data - prognostics and system health management - occupational safety - accident and incident modeling - maintenance modeling and applications - simulation for safety and reliability analysis - dynamic risk and barrier management - organizational factors and safety culture - human factors and human reliability - resilience engineering - structural reliability - natural hazards - security - economic analysis in risk managemen