Improved Ordinary Measure and Image Entropy Theory based intelligent Copy Detection Method

DOI

10.2991/ijcis.2011.4.5.4How to use a DOI?

Keywords

Ordinal Measure; Image Entropy Theory; Copy Detection.

Abstract

Nowadays, more and more multimedia websites appear in social network. It brings some security problems, such as privacy, piracy, disclosure of sensitive contents and so on. Aiming at copyright protection, the copy detection technology of multimedia contents becomes a hot topic. In our previous work, a new computer-based copyright control system used to detect the media has been proposed. Based on this system, this paper proposes an improved media feature matching measure and an entropy based copy detection method. The Levenshtein Distance was used to enhance the matching degree when using for feature matching measure in copy detection. For entropy based copy detection, we make a fusion of the two features of entropy matrix of the entropy feature we extracted. Firstly,we extract the entropy matrix of the image and normalize it. Then, we make a fusion of the eigenvalue feature and the transfer matrix feature of the entropy matrix. The fused features will be used for image copy detection. The experiments show that compared to use these two kinds of features for image detection singly, using feature fusion matching method is apparent robustness and effectiveness. The fused feature has a high detection for copy images which have been received some attacks such as noise, compression, zoom, rotation and so on. Comparing with referred methods, the method proposed is more intelligent and can be achieved good performance.

Copyright

© 2011, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - JOUR
AU  - Dengpan Ye
AU  - Longfei Ma
AU  - Lina Wang
AU  - Robert H. Deng
PY  - 2011
DA  - 2011/09/01
TI  - Improved Ordinary Measure and Image Entropy Theory based intelligent Copy Detection Method
JO  - International Journal of Computational Intelligence Systems
SP  - 777
EP  - 787
VL  - 4
IS  - 5
SN  - 1875-6883
UR  - https://doi.org/10.2991/ijcis.2011.4.5.4
DO  - 10.2991/ijcis.2011.4.5.4
ID  - Ye2011
ER  -