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A color fingerprint of video shot for content identification
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Source International Multimedia Conference archive
Proceedings of the 12th annual ACM international conference on Multimedia table of contents
New York, NY, USA
POSTER SESSION: Technical poster session 1: multimedia analysis, processing, and retrieval table of contents
Pages: 276 - 279  
Year of Publication: 2004
ISBN:1-58113-893-8
Authors
Xianfeng Yang  Institute for Infocomm Research, Singapore
Qi Tian  Institute for Infocomm Research, Singapore
Ee-Chien Chang  National University of Singapore, Singapore
Sponsors
SIGMULTIMEDIA: ACM Special Interest Group on Multimedia
ACM: Association for Computing Machinery
Publisher
ACM  New York, NY, USA
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Downloads (6 Weeks): 9,   Downloads (12 Months): 30,   Citation Count: 1
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ABSTRACT

In this paper we propose a novel space-time color feature representation for video shot and apply it to content identification. In this representation the shot is cut into <i>k</i> equal size segments, and each segment is represented by a blending image formed through averaging the pixels' values of each frame in this segment along time direction. Each blending image is then divided into equal size blocks, and two color patterns named major and minor colors among mean R,G,B are extracted for each block. Hence each shot can be represented by a fixed-length string. The experiment shows this representation is not only robust to image quality reduction, frame size and frame rate change, but also to color distortion such as brightness/contrast adjustment. We also give a video similarity measure based on this color feature to identify shot chunks. We conducted experiment on 100 video clips, and quite low error rates can be achieved when identifying small size shot chunks with significant color distortion. From the experiment we believe that this color feature is a compact and robust representation for video content, and effective for content identification.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
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Collaborative Colleagues:
Xianfeng Yang: colleagues
Qi Tian: colleagues
Ee-Chien Chang: colleagues