Authors:
Yu Bai
1
;
Li Zhuo
1
;
YingDi Zhao
1
and
Xiaoqin Song
2
Affiliations:
1
Beijing University of Technology, China
;
2
Nanjing University of Aeronautics and Astronautics, China
Keyword(s):
Itti Attention Model, K Neighbor Search, Near-duplicate Detection, Surfgram.
Related
Ontology
Subjects/Areas/Topics:
Computer Vision, Visualization and Computer Graphics
;
Features Extraction
;
Image and Video Analysis
Abstract:
The technology of near-duplicate video detection is currently a research hot spot in the field of multimedia information processing. It has great value in the areas such as large scale video information indexing and copyright protection. In the case of large-scale data, it is very important to ensure the accuracy of detection and robustness, in the meanwhile improving the processing speed of video copy detection. In this respect, a HVS(Human Visual System)-based video copy detection system is proposed in this paper.This system utilizes the visual attention model to extract the region of interest(ROI) in keyframes, which extracts the Surfgram feature only from the information in ROI, rather than all of the information in the keyframe, thus effectively reducing the amount of the data to process. The experimental results have shown that the proposed algorithm can effectively improve the speed of detection and perform good robustness against brightness changes, contrast changes, frame dr
ops and Gaussian noise.
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