DocumentCode :
2952101
Title :
Video Summarization with Global and Local Features
Author :
Guan, Genliang ; Wang, Zhiyong ; Yu, Kaimin ; Mei, Shaohui ; He, Mingyi ; Feng, Dagan
Author_Institution :
Sch. of Inf. Technol., Univ. of Sydney, Sydney, NSW, Australia
fYear :
2012
fDate :
9-13 July 2012
Firstpage :
570
Lastpage :
575
Abstract :
Video summarization has been crucial for effective and efficient access of video content due to the ever increasing amount of video data. Most of the existing key frame based summarization approaches represent individual frames with global features, which neglects the local details of visual content. Considering that a video generally depicts a story with a number of scenes in different temporal order and shooting angles, we formulate scene summarization as identifying a set of frames which best covers the key point pool constructed from the scene. Therefore, our approach is a two-step process, identifying scenes and selecting representative content for each scene. Global features are utilized to identify scenes through clustering due to the visual similarity among video frames of the same scene, and local features to summarize each scene. We develop a key point based key frame selection method to identify representative content of a scene, which allows users to flexibly tune summarization length. Our preliminary results indicate that the proposed approach is very promising and potentially robust to clustering based scene identification.
Keywords :
pattern clustering; video signal processing; clustering based scene identification; frame identification; global features; key frame based summarization approach; key point based key frame selection method; local features; representative content identification; scene summarization; shooting angles; summarization length tuning; temporal order; two-step process; video content; video frame; video summarization; visual similarity; Analytical models; Clustering algorithms; NASA; Redundancy; Robustness; Semantics; Visualization; Clustering; Keypoint pool; Local features; Scene identifi Video summarization; cation;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Multimedia and Expo Workshops (ICMEW), 2012 IEEE International Conference on
Conference_Location :
Melbourne, VIC
Print_ISBN :
978-1-4673-2027-6
Type :
conf
DOI :
10.1109/ICMEW.2012.105
Filename :
6266446
Link To Document :
بازگشت