Uma abordagem baseada em características de cor para a elaboração automática e avaliação subjetiva de resumos estáticos de vídeos
Advances in compression techniques, in decreasing cost of storage, and in high-speed transmission have facilitated the way videos are created, stored and distributed. The increase in the amount of video data deployed and used in many applications, such as search engines and digital libraries, reveal...
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| Tipo de recurso: | tesis de maestría |
| Estado: | Versión publicada |
| Fecha de publicación: | 2008 |
| País: | Brasil |
| Institución: | Universidade Federal de Minas Gerais (UFMG) |
| Repositorio: | Repositório Institucional da UFMG |
| Idioma: | portugués |
| OAI Identifier: | oai:repositorio.ufmg.br:1843/RVMR-7LKLEM |
| Acceso en línea: | http://hdl.handle.net/1843/RVMR-7LKLEM |
| Access Level: | acceso abierto |
| Palabra clave: | Processamento de imagens Sistemas de recuperação da informação Videodigital Indexação automatica Visão por computador Computação Dispositivos de armazenamento otico Reconhecimento de padrões |
| Sumario: | Advances in compression techniques, in decreasing cost of storage, and in high-speed transmission have facilitated the way videos are created, stored and distributed. The increase in the amount of video data deployed and used in many applications, such as search engines and digital libraries, reveals not only the importance as multimedia data type, but also leads to the requirement of efficient management of video data. This management paved the way for new research areas, such as video summarization. Essentially, this research area consists of automatic generating a short summary of a video, which can either be static summary (keyframes set) or dynamic summary (set of video segments). This work presents a methodology for the development of static summaries. The method is based on color feature extraction from video frames and unsupervised classification. The video summaries produced are evaluated by users and compared with approaches found in the literature. With a confidence level of 98%, the proposed solution provided results with superior quality in relation to the approaches compared. |
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