Video fingerprinting based on graph model

Authors:
Xiushan Nie;Jiande Sun;Zhihui Xing;Xiaocui Liu
Affiliations:
School of Computer Science and Technology, Shandong University of Finance and Economics, Jinan, China 250014;School of Information Science and Engineering, Shandong University, Jinan, China 250100;School of Information Science and Engineering, Shandong University, Jinan, China 250100;School of Information Science and Engineering, Shandong University, Jinan, China 250100
Venue:
Multimedia Tools and Applications
Year:
2014

Citing 9
Cited 0

Normalized Cuts and Image Segmentation

IEEE Transactions on Pattern Analysis and Machine Intelligence
Feature Extraction and a Database Strategy for Video Fingerprinting

VISUAL '02 Proceedings of the 5th International Conference on Recent Advances in Visual Information Systems
What Energy Functions Can Be Minimizedvia Graph Cuts?

IEEE Transactions on Pattern Analysis and Machine Intelligence
Better Foreground Segmentation for Static Cameras via New Energy Form and Dynamic Graph-cut

ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 04
Video sequence matching using singular value decomposition

ICIAR'06 Proceedings of the Third international conference on Image Analysis and Recognition - Volume Part I
A Robust and Fast Video Copy Detection System Using Content-Based Fingerprinting

IEEE Transactions on Information Forensics and Security
Spatio–Temporal Transform Based Video Hashing

IEEE Transactions on Multimedia
Video summarization and scene detection by graph modeling

IEEE Transactions on Circuits and Systems for Video Technology
Efficient Mining of Multiple Partial Near-Duplicate Alignments by Temporal Network

IEEE Transactions on Circuits and Systems for Video Technology

Quantified Score

Hi-index	0.00

Visualization

Abstract

A robust video fingerprinting based on graph model is proposed in this paper, where two graph models are constructed for key frames selection and foreground extraction, respectively. First, the video is represented as a complete undirected graph and a binary tree is formed using normalized cut algorithm to select key frames. Then, the pixels of each key frame are modeled as a Markov Random Field and another graph model is formed to extract foreground by graph cut. Finally, the fourth-order cumulant of foreground is computed to generate video fingerprints. Experimental results show that the proposed algorithm has good robustness and discrimination.