A considerable number of videos are illegal copies or manipulated versions of existing media, making copyright management a complicated process. Call for Change:- Today’s widespread video copyright infringement calls for the development of fast and accurate copy-detection algorithms. As video is the most complex type of digital media, it has so far received the least attention regarding copyright management. Protect Data:- Content-based copy detection (CBCD) ,a promising technique for video monitoring and copyright protection.

1. A Segmentation based Sequential
Pattern Matching for
Efficient Video Copy Detection

2. • Introduction
• Motivation
• Problem Statement
• Aim and Objectives
• Literature Survey
• System architecture
• Proposed System
• Improvements
• References

3.  Rapid growth of the Internet , Easiness in digital media acquiring and
distributing.
 As digital videos can be copied and modified easily, protecting the
copyright of the digital media has become matter of concern.
 Criteria for selection of copy detection algorithm :-
 Accuracy, measured in terms of false positive and false negative rates
 Computational Requirements, processing time & storage
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4. Definition-“Exclusive rights granted by the State for
inventions, new and original designs, trademarks,
new plant varieties and artistic and literary works”.
Goals For IPR Security:-
Detection and retrieval of authentic content.
Protection of content from fraudulent
alterations.
Common types of intellectual property rights
include copyright ,trademarks, patents, industrial
design rights.
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5. A point in an image which has a well-defined position and can
be robustly detected.
Local features vs. Global Features
Types of local feature - Edges, Corner, Blobs.
Associated with a significant change of one or more image
properties (e.g.intensity,colors).
 Used to find corresponding points between images which is
very useful for numerous applications!

6. Courtesy:- Kristen Grauman

8. Motivation
• Main Concern-
• A considerable number of videos are illegal copies or manipulated versions of
existing media, making copyright management a complicated process.
• Call for Change:-
• Today’s widespread video copyright infringement calls for the development of
fast and accurate copy-detection algorithms.
• As video is the most complex type of digital media, it has so far received the
least attention regarding copyright management.
• Protect Data:-
• Content-based copy detection (CBCD) ,a promising technique for video
monitoring and copyright protection.

9. Problem statement
To design a copy-detection algorithm which is
sufficiently robust to detect severely deformed
copies with high accuracy to localize copy segment.

10. Aim and Objectives of the project
• The aim of the project is to provide security to
multimedia content and provide platform to safeguard
copyright of the digital media.
• Objective of video copy detection:-
• To decide whether a query video segment is a copy of a
video from the video data set.
• If a system finds a matching video segment, then to
retrieve the name of copy video in the video database
and the time stamp where the query was copied from.

11. Architecture for Video Copy Detection
• Main Components:-
1. Change-based Threshold for Video Segmentation
2. Feature extraction with SIFT from keyframes
3. Similarity-based matching between SIFT feature point sets
4. Graph-based Video Sequence Matching
5. Evaluation Criteria:
• Copy Location Accuracy
• Computational Time Cost
• Recall & Precision

12. Architecture for Video Copy Detection
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1. Change-based Threshold for Video Segmentation
2. Feature extraction with Binary SIFT from keyframes
3. Similarity-based matching between SIFT feature point sets
4. Graph-based Video Sequence Matching

13. 1.Change-basedThresholdforVideo Segmentation
Method cuts continuous video frames into video segments by eliminating
temporal redundancy of the visual information of continuous video frames.
• Threshold for detecting abrupt changes of visual information of frames , TH= µ
+α σ , µ and σ are mean and standard deviation of difference values between
consecutive frames , α suggested between 5 and 6.
• Threshold for detecting gradual changes of visual information of frames , TL =
b * Th , where b is selected from the range 0.1-0.5

14. 2. FeatureExtractionwithSIFT fromKeyframes
SIFT Detection:-
1. Find Scale-Space Extrema
2. Keypoint Localization & Filtering
– Improve keypoints,throw out bad ones
SIFT Description:-
3. Orientation Assignment
– Remove effects of rotation and scale
4. Create descriptor
– Using histograms of orientations

15. 3. Similarity-basedmatchingbetweenSIFT featuresets
• For Binary SIFT descriptor extracted compute its nearest
neighbor in the dictionary.
• Cluster the set of descriptors (using k-means for
example) to k clusters. The cluster centers act as
dictionary’s visual words.
• Given a test feature(Binary SIFT),Hierarchical k-NN
search is used to find out nearest visual word.

16. 4. Graph-based Video Sequence Matching
Time direction consistency: For Mij and Mlm, if there exists (i – j)*(l-m)> 0, then Mij and Mlm
satisfy the time direction consistency.
Time jump degree: For Mij and Mlm ,the time jump degree between them is defined as,
If the following two conditions are satisfied, there exists an edge between two vertexes:
1. The two vertexes should satisfy time direction consistency.
2. The time jump degree ∆t < T( T is a preset threshold).

17. Evaluation Criteria:-
• Copy location accuracy:
• This measure aims to assess the accuracy of finding the exact extent of the
copy in the reference video.
• What percent of your predictions were correct?
 Precision :

true number of copied images detected
total number of copied images detected
 What percent of positive predictions were correct?
 Recall:

true number of copied images detected
total number of real copied images in database
 What percent of the positive cases did you catch?

18. Improvements In Existing System
• Segmentation based on Dual Threshold:
• Basic Problems:-
• Matching based on SIFT descriptor is computationally expensive for large number of points and its high
dimension.
• To reduce the computational complexity, use the dual-threshold method to segment the videos into segments with
homogeneous content and extract keyframes from each segment.
• Binary SIFT Descriptor for Feature Matching:
• Basic Problems:-
• Global or Local Descriptor?
• SIFT not only has good tolerance to scale changes, illumination variations, and image rotations, but also is robust to
change of viewpoints, and additive noise,logo insertion, shifting or cropping, complicated edit.
• Compared with methods based on global descriptor, methods based on local descriptor(SIFT) have a better
detection performance .
• Memory cost of binary SIFT is low, making it feasible to store the whole binary SIFT in the index list.
• Graph-based Video Sequence Matching:
• Basic Problems:-
• Hard threshold ?
• Exhaustive search ?
• To resolve these problems, Graph-based video sequence matching method has the advantages of high accuracy in
locating copies, reducing detection time costs, and being able to simultaneously locate more than one copy in two
comparing video sequences.

19. References
• “A Segmentation And Graph-based Video Sequence Matching Method For Video Copy
Detection”,hong Liu, Hong Lu, And Xiangyang Xue, IEEE Transactions On Knowledge And Data
Engineering, Vol. 25, No. 8, August 2013
• “Visual word expansion and BSIFT verification for large-scale image search”,Wengang Zhou •
Houqiang Li • Yijuan Lu •Meng Wang • Qi Tian,Springer,2013
• “Content-based Video Copy Detection Using Discrete Wavelet Transform”, Gitto George Thampi,
D. Abraham Chandyc., Proceedings Of IEEE Conference On Information And Communication
Technologies,2013
• “Fast And Accurate Content-based Video Copy Detection Using Bag-of-global Visual
Features”,yusuke Uchida, Koichi Takagi, Shigeyuki Sakazawa,IEEE,2012
• “Fast And Robust Short Video Clip Search For Copy Detection” Junsongyuan, Ling-yu Duan, Qi Tian,
Surendra Ranganath, And Changsheng Xu1,2005
• “Distinctive Image Features From Scale-invariant Keypoints,” Int’l J. Computer Vision,
D.G.Lowe,Vol. 60, No. 2, Pp. 91-110, 2004.