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Please use this identifier to cite or link to this item: http://hdl.handle.net/UCSP/15869
Title: A New Method for Static Video Summarization Using Local Descriptors and Video Temporal Segmentation
Authors: Cayllahua Cahuina, Edward
Camara Chavez, Guillermo
Keywords: Digital video content;Exponential growth;Local descriptors;State-of-the-art methods;Temporal segmentations;Temporal video segmentation;Video summarization;Video temporal segmentation;Computer graphics;Multimedia systems;Semantics;Video signal processing;Video recording
Issue Date: 2013
Publisher: Scopus
metadata.dc.relation.uri: https://www.scopus.com/inward/record.uri?eid=2-s2.0-84891532974&doi=10.1109%2fSIBGRAPI.2013.39&partnerID=40&md5=a0affe279540362a411601282736ba96
Abstract: The continuous creation of digital video has caused an exponential growth of digital video content. To increase the usability of such large volume of videos, a lot of research has been made. Video summarization has been proposed to rapidly browse large video collections. To summarize any type of video, researchers have relied on visual features contained in frames. In order to extract these features, different techniques have used local or global descriptors. In this paper, we propose a method for static video summarization that can produce meaningful and informative video summaries. We perform an evaluation using over 100 videos in order to achieve a stronger position about the performance of local descriptors in semantic video summarization. Our experimental results show, with a confidence level of 99%, that our proposed method using local descriptors and temporal video segmentation produces better summaries than state of the art methods. We also demonstrate the importance of a more elaborate method for temporal video segmentation, improving the generation of summaries, achieving 10% improvement in accuracy. We also acknowledge a marginal importance of color information when using local descriptors to produce video summaries. © 2013 IEEE.
URI: http://repositorio.ucsp.edu.pe/handle/UCSP/15869
ISBN: urn:isbn:9780769550992
ISSN: 15301834
Appears in Collections:Artículos - Ciencia de la computación

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