Detalhes bibliográficos
Ano de defesa: |
2016 |
Autor(a) principal: |
Martins, Guilherme Brandão [UNESP] |
Orientador(a): |
Não Informado pela instituição |
Banca de defesa: |
Não Informado pela instituição |
Tipo de documento: |
Dissertação
|
Tipo de acesso: |
Acesso aberto |
Idioma: |
por |
Instituição de defesa: |
Universidade Estadual Paulista (Unesp)
|
Programa de Pós-Graduação: |
Não Informado pela instituição
|
Departamento: |
Não Informado pela instituição
|
País: |
Não Informado pela instituição
|
Palavras-chave em Português: |
|
Link de acesso: |
http://hdl.handle.net/11449/139458
|
Resumo: |
Currently, a number of improvements related to computational networks and data storage technologies have allowed a considerable amount of digital content to be provided on the internet, mainly through social networks. In order to exploit this context, video processing and pattern recognition approaches have received a considerable attention in the last years. Movie recommendation systems are widely employed in virtual stores, thus being one of the main applications regarding to research advances in the video processing field. Aiming to boost the content recommendation and storage cutback, different video categorization and video summarization techniques have been applied to handle with more informative and redundant content. By availing clustering and data description techniques, it is possible to identify keyframes from a given sample collection in order to consider them as part of the video summarization process. Furthermore, through labeled video data collections it is possible to classify samples in order to arrange them by video genres. The main goal of this work is to employ the Optimum-Path Forest classifier in both video summarization and video genre classification processes as well as to conduct a viability study of such classifier in the aforementioned contexts. The results have shown this classifier can achieve promising performances, being very close in terms of summary quality and consistent recognition rates to some state-of-the-art video summarization and classification approaches. |