Please use this identifier to cite or link to this item: http://www.repositorio.ufop.br/handle/123456789/4370
Title: A unified approach to content-based indexing and retrieval of digital videos from television archives.
Authors: Souza, Celso L. de
Pádua, Flávio Luis Cardeal
Nunes, Cristiano Fraga Guimarães
Assis, Guilherme Tavares de
Silva, Giani David
Keywords: Television archives
Retrieval vídeo
TV broadcast structuring
Video indexing
Issue Date: 2014
Citation: SOUZA, C. L. de et al. A unified approach to content-based indexing and retrieval of digital videos from television archives. Artificial Intelligence Research, v. 3, p. 49-61, 2014. Disponível em: <http://www.sciedu.ca/journal/index.php/air/article/view/5251/3240>. Acesso em: 22 jan. 2015.
Abstract: This work addresses the development of a unified approach to content-based indexing and retrieval of digital videos from television archives. The proposed approach has been designed to deal with arbitrary television genres, making it suitable for various applications. To achieve this goal, the main steps of a content-based video retrieval system are addressed in this work, namely: video segmentation, key-frame extraction, content-based video indexing and the video retrieval operation itself. Video segmentation is addressed as a typical TV broadcast structuring problem, which consists in automatically determining the boundaries of each broadcasted program (like movies, news, among others) and inter-program (for instance, commercials). Specifically, to segment the videos, Electronic Program Guide (EPG) metadata is combined with the detection of two special cues, namely, audio cuts (silence) and dark monochrome frames. On the other hand, a color histogram-based approach performs key-frame extraction. Video indexing and retrieval are accomplished by using hashing and k-d tree methods, while visual signatures containing color, shape and texture information are estimated for the key-frames, by using image and frequency domain techniques. Experimental results with the dataset of a multimedia information system especially developed for managing television broadcast archives demonstrate that our approach works efficiently, retrieving videos in 0.16 seconds on average and achieving recall, precision and F1 measure values, as high as 0.76, 0.97 and 0.86 respectively.
URI: http://www.repositorio.ufop.br/handle/123456789/4370
metadata.dc.identifier.doi: https://doi.org/10.5430/air.v3n3p49
ISSN: 1927-6974
metadata.dc.rights.license: Os trabalhos publicados na Artificial Intelligence Research estão sob Licença Creative Commons que permite copiar, distribuir, transmitir e adaptar o trabalho, desde que sejam citados o autor e licenciante. Fonte: Artificial Intelligence Research <http://www.sciedu.ca/journal/index.php/air/index>. Acesso em: 17 out. 2016.
Appears in Collections:DECOM - Artigos publicados em periódicos

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