Detecção de vídeos não-colaborativos com base no conteúdo visual em redes sociais para compartilhamento de vídeo

• Ano de defesa: 2012
• Autor(a) principal: Antonio da Luz Junior
• Tipo de documento: Tese
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal de Minas Gerais; UFMG
• Palavras-chave em Português: SIFT; Redes sociais; Classificação Semântica de vídeos; LSA; STIP; Semântica; Computação; Redes sociais on-line; Vídeo digital
• Link de acesso: http://hdl.handle.net/1843/ESBF-8TMMP8

In this work we are concerned with detecting non-collaborative videos in video sharing social networks. Specifically, we investigate how much visual content-based analysis can aid in detecting ballot stuffing and spam videos in threads of video responses. That is a very challenging task, because of the high-level semantic concepts involved; of theassorted nature of social networks, preventing the use of constrained a priori information; and, which is paramount, of the context-dependent nature of non-collaborative videos. Content filtering for social networks is an increasingly demanded task: due to their popularity, the number of abuses also tends to increase, annoying the user and disruptingtheir services. We propose a context-aware description, which improves detection considerably in comparison with the baseline bags-of-visual-words model, by allowing us to incorporate the context of the video into the representation. Our model is evaluated in two challenging video dataset and show the feasibility of the proposed approaches.