International Journal of Engineering Trends and Technology (IJETT) – Volume 9 Number 1 - Mar 2014
The Way to Preserve Copyrights for Video Data and
Elimination of Untrusted User’s from the Network
K. Gowthami1, Kavitha Esther Rajakumari2
M.E Computer Science and Engineering1, Faculty of Computing2,Sathyabama University1, Sathyabama University2
Chennai-600119, India
ABSTRACT-Due to rapid development in the field of
multimedia there is a scope for redundancy in video
data. So copyright violation is encountered as a serious
problem. There exist a large amount of data which is
manipulated and ill legal copies have been published in
many social network sites (e. g. YouTube). In order to
prohibit this redundant video data many methods have
been proposed. Primary methods are based on water
marking, content based copy detection (CBCD) and
SIFT. In water marking, if the original image is not
water marked it cannot detect the copied images. In
content based copy detection, signature limitation has
become a problem. Scale Invariant Feature Transform
(SIFT) encounters a problem of computational cost and
is not ease for large applications. In this paper three
methods are proposed viz. Auto Dual threshold method,
SIFT Singular Value Decomposition (SVD) and Graph
based method. By applying these methods redundant
frames can be eliminated, feature extraction made easy
with SVD and matching the original data with copied
data.
Keywords— Water Marking, CBCD, Auto Dual Threshold,
SVD, SIFT, Graph Based Sequence method.
I. INTRODUCTION
In this proposed work, a segmentation and graphbased video sequence matching method for Content Based
Video Copy Detection. In particularly, SIFT (Scale
Invariant Feature Transform) descriptor for video content
description is utilized. But, the matching based on SIFT
descriptor is quite computational expensive to extract the
robust key point descriptors and perform pair wise
matching of a large number of local features frame by
frame. So that, initially the dual-threshold method is
utilized and it is segmenting the videos into segments.
After that, utilized an SVD-based technique to match two
video frames with the SIFT point set descriptors. Finally, to
get the video sequence matching result after then propose a
graph- based method. It can convert the video succession
ISSN: 2231-5381
matching into the longest path in the frame matching-result
with time limit.
The proposed work shows that the segmentation
and graph-based video sequence matching methods can
detect video copies efficiently. Here the server will identify
misbehaving user in the proposed system and block the
user for particular time.
II. RELATED ARTICLES
Many content-based video copy detection
methods have been proposed in addition, copy is a subset
of near replica. There are many methods proposed on nearduplicate detection.
Z.Huang, H.T.Shen, J.Shao, B.Cui (2010)
developed a paper for Practical Online Near-Duplicate
Subsequence Detection for continuous video streams. Here
Water-Marking Technique is used. CBCD has a signature
has limitation; example can be two distinct broadcasts.
Hence, proposed to transform a video stream in to
one-dimensional Video Distance Trajectory (VDT)
monitoring continues changes of consecutive frames with
respect to a reference point, which is further segmented and
represented by a series of compact signatures called Linear
Smoothing function.
O.Kucuktunc, U.Gudukbay (2010) developed a
paper for Fuzzy Color Histogram –Based video
segmentation. Here a video is composed of several shots
with abrupt or gradual changes. For frame similarity
Discrete Cosine Transform is being used. For identifying
PiP, small changes efficient detection algorithm used.
Hampapur et al. compared distance measures and
video sequence matching methods for video copy
detection. They employed convolution for motion direction
feature, L1 distance for ordinal intensity signature (OIS),
and histogram intersection for color histogram feature. The
consequences show that the method using OIS performs
better. Yuan et al. collective OIS with color histogram
feature as a tool for describing video sequence.
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International Journal of Engineering Trends and Technology (IJETT) – Volume 9 Number 1 - Mar 2014
Particularly, they divided each video frames the length of
the time sequence into several blocks and proposed average
gray values for each block. Then, they associated gray
values of these alienated blocks individually along the time
direction before they use those sequence information to
describe the video content.An early method for detection of
video data, based on colour histogram intersection is
proposed by Satoh. Yeh and Cheng use a method that
divides the picture in to four regions, and mines a Markov
Stationary Feature (MSF)-extended HSV colour histogram.
Basharat et al. present a video-matching
framework using spatio-temporal segmentation. A set of
features (colour, texture, motion, and SIFT descriptors) is
extracted from each segment, and the comparison between
two videos is computed with a bipartite graph and Earth
Mover’s Distance (EMD).
Williams et al. propose a video copy detection
method based on efficiently matching local spatiotemporal
feature points with a disk-based indexing scheme. In
general, extracting and matching points of interest are
costly operations in terms of computation time.
Bertini et al. present a clip-matching algorithm
that use video fingerprint based on standard MPEG7descriptors. An effective combination of color layout
descriptor (CLD), scalable color descriptor (SCD), and
edge histogram descriptor (EHD) forms the finger print.
Fingerprints are extracted from each clip, and they are
compared using an edit distance. Sarkar et al. use CLD as
video fingerprints and propose a non-metric distance
measure to efficiently search for matching videos in high
dimensional space.
III. SUMMARY OF EXISTING SYSTEM
In existing system SIFT descriptor is being used
for video content description. In particularly, SIFT (Scale
Invariant Feature Transform) descriptor for video content
description is utilized to the reason of the Discriminative
ability and good stability of local features. However,
matching based on SIFT descriptor is computationally
expensive for large number of points and the high
dimension.
Drawbacks in the existing system are server will
not identify the misbehaving user and also user tries to
login and same query is being published many times.
ISSN: 2231-5381
Fig 1: Functional Architecture
Server monitors each and every users query
videos. It will continuously monitor the query videos of
each and every user’s communication. When monitoring
the server will identify the copied frames from the input
queries i.e. the matching frame result is been verified from
the database which is already been trained. It identifies the
unauthorized user and block that identified user at
particular time Here additional opportunity is provided to
all users’ i.e. (user’s login limitation). Suppose if attempt of
user is crossed the limit then that particular user will
discard from the network.
IV. PROPOSED SYSTEM
For categorizing redundant video data effectively
and efficiently, initially the dual-threshold method is
exploited and it is slicing the videos into segments with the
at ease of homogeneous and then the key frame is extract
from every segment. The SIFT features are extracted from
that segments of key frames. After that, exploited an SVDbased technique to match two video frames with the SIFT
point set descriptors. Absolutely, to get the video sequence
matching outcome propose a graph- based method. It can
transfer the video sequence matching into discovering the
best ever path in the frame matching-result with time
constriction. If the query video is equivalences then servers
identifies the illicit user and block that spotted user at
certain time. And over again if user prolongs to upload
video then user will be isolated from network.
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International Journal of Engineering Trends and Technology (IJETT) – Volume 9 Number 1 - Mar 2014
V. IMPLEMENTATION DETAILS
D. SIFT-SVD
A. User’s Video Query
Query videos are analyzed. Features are extracted
from these videos and compared to those stored in the
referential database. The matching results are then analyzed
and the detection results are returned.
B. Extraction and Compression of frames
For analyzing the video frames are being extracted
and Compression is the decrease in size of data in order to
save space or transmission time. Graphic image
compression can be either lossy or lossless. A lossless
frame compression is done.
The use SVD method to measure the similarity
between two SIFT feature point sets, and emphasize the
similarity of “frame-to-frame”. Specifically, the goal of the
SVD method to reduce and correct the wrong match
between the two points in two SIFT feature sets.
Characteristics:
1. Transposition and substitute
2. Energy Concentricity.
invariance.
E. Graph Method for Video Similarity Checking
The graph-based video sequence matching method for
video copy detection. The method is obtainable as follows:
C. Elimination of Redundant Frames
1.
2.
3.
Fragment the video frames and extract features of
the key frames.
Go with the query video and target video.
Produce the matching result graph according to
the matching results.
- Time direction consistency: Condition 1 indicates that if
the query video is a copy deriving from the target video,
then the video subsequence temporal order amid query
video and target video must be consistent, which is
reasonable in real application.
Fig 2: Imitative Image
The above image is the replica of original image.
So this frame is the redundant frame and this should be
identified using Auto Dual Threshold method and should
be removed.
-Time jump degree: If Condition 1 is satisfied, Condition 2
is used to limit the time span of two matching results
between the query video and the target video. If the time
span exceeds a assured threshold, it is considered that there
does not exist certain correlation between the two matching
results.
F. Secure Accessing
If the user’s video is matched with original video
then user will be blocked for certain time limit and again if
user continues to upload video then user will be removed
from network.
Fig 3: Novel Image
An auto dual-threshold method to eliminate
redundant video frames. This method cuts incessant video
frames into video segments by eliminating temporal replica
of the visual information of continuous video frames. This
method has the subsequent two characteristics. First, two
thresholds are used. Particularly one threshold is used for
detecting sudden changes of visual information of frames
and another for regular changes. Second, the values of two
thresholds are deter-mined adaptively according to video
content. The auto dual-threshold method to remove the
redundant frames.
ISSN: 2231-5381
VI. PERFORMANCE EVALUTION
From the view of video signature, LSF and
CONT-CONX use the global signature, and SVD-SIFTGRAPH and MRF use the local signature even though the
methods based on global signature can detect copy videos
to some level, its capability to identify the copy videos with
complex transformations (such as PiP, Shift, crop) is
limited. Thus, in usefulness, the methods based on local
signature execute better than the ones based on global
signature. On the other hand, with respect to competence
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International Journal of Engineering Trends and Technology (IJETT) – Volume 9 Number 1 - Mar 2014
the methods based on global signature (LSF, CONTCONX) have more advantages over the ones based on local
signature (SVD-SIFT-GRAPH, MRF).
The following graph shows the comparison among
the methods mentioned above in terms of effectiveness and
efficiency.
REFERENCES
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Fig 4: Comparison Among Methods
From the graph it is shown SIFT-SVD can obtain
a better trade-off between the effectiveness and the
efficiency.
1-SIFT-SVD
2-LSF
3-CONT-CONX
4-MRF
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VII. CONCLUSION
This paper shows that three methods are proposed
viz. Auto Dual threshold method, SIFT Singular Value
Decomposition (SVD) and Graph based method. By
applying these methods redundant frames can be
eliminated, feature extraction made easy with SVD and
matching the original data with copied data and SIFT-SVD
can obtain a better trade-off between the effectiveness and
the efficiency. Additionally. the server will identify
misbehaving user in the proposed system and block the
untrusted .user from the network.
[13] A. Hampapur and R. Bolle, “Comparison of Distance Measures for
Video Copy Detection,” Proc. IEEE Int’l Conf. Multimedia and Expo
(ICME), pp. 188-192, 2001.
ACKNOWLEDGMENT
We would like to thank Dr.B.Bharathi, Head of
the Department, Department of Computer Science and
Engineering, Ms.Kavitha Esther Rajakumari for her
encouragement and support.
ISSN: 2231-5381
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