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| Author(s): |
Bruno Lorenço Lopes
Total Authors: 1
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| Document type: | Master's Dissertation |
| Press: | São Carlos. |
| Institution: | Universidade de São Paulo (USP). Instituto de Ciências Matemáticas e de Computação (ICMC/SB) |
| Defense date: | 2014-04-28 |
| Examining board members: |
Rudinei Goularte;
Maria da Graça Campos Pimentel;
Roberto Willrich
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| Advisor: | Rudinei Goularte |
| Abstract | |
Many Computational Science areas (Content Personalization and Adaptation, Information Retrieval, among other) benefit from video segmentation in smaller information units. The literature reports lots of techniques and methods, whose goal is to identify these units. One of these techniques limitations is that they dont handle scene detection in semantically complex segments, which are defined as video snippets that present more than one subject or theme, whose latent semantics can hardly be determined using only one media. Those segments are very relevant, since they are present in multiple video domains as movies, news and even television commercials. This Masters dissertation proposes a video scene segmentation technique able to detect scenes in semantically complex segments. In order to achieve this goal it uses latent semantics extracted by the Bag of VisualWords to group a video segments. This grouping process is based on multimodality, through the visual and aural features analysis, and their results combination using late fusion strategy. This works demonstrates technical feasibility in recognizing scenes in semantically complex segments (AU) | |
| FAPESP's process: | 11/05238-0 - Scenes Detection into Semantically Complex Segments |
| Grantee: | Bruno Lorenço Lopes |
| Support Opportunities: | Scholarships in Brazil - Master |