A Fast and Robust Key-Frames based Video Copy Detection
Using BSIF-RMI
Yassine Himeur, Karima Ait-Sadi and Abdelmalik Oumamne
Centre de Développement des Technologies Avancées (CDTA), Division TELECOM, Alger, Algerie
Keywords: Video Copy Detection, Key-Frames, Gradient Magnitude Similarity Deviation, Binarized Statistical Image
Features, Relative Mean Intensity.
Abstract: Content Based Video Copy Detection (CBVCD) has gained a lot of scientific interest in recent years. One
of the biggest causes of video duplicates is transformation. This paper addresses a fast video copy detection
approach based on key-frames extraction which is robust to different transformations. In the proposed
scheme, the key-frames of videos are first extracted based on Gradient Magnitude Similarity Deviation
(GMSD). The descriptor used in the detection process is extracted using a fusion of Binarized Statistical
Image Features (BSIF) and Relative Mean Intensity (RMI). Feature vectors are then reduced by Principal
Component Analysis (PCA), which can more accelerate the detection process while keeping a good
robustness against different transformations. The proposed framework is tested on the query and reference
dataset of CBCD task of Muscle VCD 2007 and TRECVID 2009. Our results are compared with those
obtained by other works in the literature. The proposed approach shows promising performances in terms of
both robustness and time execution.
1 INTRODUCTION
CBVCD is proposed as an alternative or a
complementary to the watermarking technology. It
can detect copies without inserting any information
and without altering the multimedia content (Lian et
al., 2010). Unlike digital watermarking, Content
based copy detection (CBCD) relies only on a
similarity comparison of content between the
original video and its various possible copies.
This technology is based on the fact that a media
visually contains enough information for detecting
copies. Therefore, the problem of CBCD is
considered as video similarity detection by using the
visual similarities of video clips.
Detection of a video copy in large video database
is not an easy task because of the size of video data.
By reducing the size of data that represents each
video in the database, the video database
manipulations such as indexing, copy detection and
fingerprinting are accelerated. In fact, not all frames
from a video sequence are equally important. A few
informative frames that characterize the action for
recognition are required. The reasons are; some
video frames are irrelevant to the underlying
activity, e.g. the frames with no action in them. They
could be nuisance for the recognition. Also, the
recognition speed can be greatly improved by using
the informative key-frames without losing important
information.to enable efficient representation and
detection of digital video, many key-frames
extraction techniques have been developed (Sujatha
and Mudenagudi, 2011).
In this work, query and dataset video are
systematically and efficiently reduced via a frame
selection procedure which use GMSD (Xue et al.,
2014) to detect key-frames in a video stream.
Further refinement in the frame selection step is
achieved using a robust feature representation based
upon BSIF and the RMI of the selected subset of
decoded frames. The procedure is presented in detail
in the following sections.
The paper is organized as follows. We first
describe related work in this field. Section 3 presents
the main contribution of the paper including the key-
frames extraction process and the feature extraction
descriptor based on BSIF and RMI. We then present
a fast video copy detection framework. In Section 4,
we provide the effectiveness of the proposed
approach based on the experimental evaluation and
the comparison to other works. Finally, discussion
and concluding remarks are given in Section 5.
40
Himeur Y., Ait-Sadi K. and Oumamne A..
A Fast and Robust Key-Frames based Video Copy Detection Using BSIF-RMI.
DOI: 10.5220/0005060000400047
In Proceedings of the 11th International Conference on Signal Processing and Multimedia Applications (SIGMAP-2014), pages 40-47
ISBN: 978-989-758-046-8
Copyright
c
2014 SCITEPRESS (Science and Technology Publications, Lda.)
2 RELATED WORK
Most video copy detection algorithms based on
global feature extract low-level feature from the
video images to represent the video, but these
algorithms are sensitive to various copy techniques,
so the detection result is not satisfactory. In contrast
to the global features, the local feature describes the
structure and texture information of neighborhood of
the interest point (Joly et al., 2007), having a good
robustness generally to brightness, viewing angle,
geometry and affine transformations. The techniques
based on local feature are divided into five types:
spatial methods, temporal methods, spatial-temporal
methods, transform-domain methods and color
methods.
On the other hand video copy detection
approaches can be classified into two large groups.
The first group includes non-key-frames based
approaches which used the whole video sequence in
the detection process. Jiang et al. (Jiang et al., 2013)
proposed a rotation invariant VCD approach; each
selected frame is partitioned into certain rings. Then
Histogram of Gradients (HOG) and RMI are
calculated as the original features. In (Cui et al.,
2010), a fast CBCD approach based on the Slice
Entropy Scattergraph (SES) is proposed. SES
employs video spatio-temporal slices which can
greatly decrease the storage and computational
complexity. Yeh et al. (Yeh et al., 2009) proposed a
frame-level descriptor for Large scale VCD. The
descriptor encodes the internal structure of a
video frame by computing the pair-wise
correlations between geometrically pre-indexed
blocks. In (Wu et al., 2009), Wu et al. introduced a
Self-Similarity Matrix (SSM) based video copy
detection scheme and a Visual Character-String
(VCS) descriptor for SSM matching. Then in (Wu et
al., 2009), the authors added a transformation
recognition module and used a self-similarity matrix
based near-duplicate video matching scheme. By
detecting the type of transformations, the near-
duplicates can be treated with the ‘best’ feature
which is decided experimentally. In (Ren et al.,
2012), Ren et al. proposed a compact video
signature representation as time series for either
global feature or local feature descriptors. It
provides a fast signature matching through major
incline-based alignment of time series.
The Second group contains key-frames based
techniques. Zhang et al. (Zhang et al., 2010)
proposed a CBVCD based on temporal features of
key-frames. Chen et al. (Chen et al., 2011)
introduced a new video copy detection method based
on the combination of video Y and U spatiotemporal
feature curves and key-frames. Tsai et al. (Tsai et al.,
2009) developed a practical CBVCD After locating
the visually similar key-frame, the
methods of Vector Quantization (VQ) and Singular
Value Decomposition (SVD) is applied to extract the
spatial features of these frames. Then, the shot
lengths are used as the temporal features for further
matching to achieve a more accurate result. In
(Chaisorn et al., 2010), Chaisorn et al. proposed
framework composed of two levels of bitmap
indexing. The first level groups videos (key-
frames) into clusters and uses them as the first
level index. The video in question need only be
matched with those clusters, rather than the entire
database. In (Kim et Nam, 2009), Kim et al.
presented a method that uses key-frames with abrupt
changes of luminance, then extracts spatio-temporal
compact feature from key-frames. Comparing with
the preregistered features stored in the video
database, this approach distinguishes whether an
uploaded video is illegally copied or not.
3 PROPOSED VIDEO COPY
DETECTION MODEL
As aforementioned above most CBVCD system
consist of three major modules: Key-frames
extraction, Extraction of fingerprint (feature vector)
and sequence matching. Fingerprint must fulfill the
diverging criteria such as discriminating capability
and robustness against various signal distortion.
Sequence matching module bears the responsibility
of devising the match strategy and verifying the test
sequence with likely originals in the database. The
architecture of our proposed CBVCD system is
shown in Figure 1.
3.1 Key-Frames Extraction Process
In this paper, Key-frames extracted from each video
shot are based on visual attention and structural
similarity. The approach produces a gradient
magnitude similarity maps from each frame. The
similarity of the maps is then measured using a
novel signal fidelity measurement, called Gradient
Magnitude Similarity Deviation (Xue et al., 2014) .
A frame will be chosen as key-frame if the value
exceeds certain threshold.
GMSD is used to estimate global variation of
gradient based local quality map for overall image
quality prediction. It is proved in (Xue et al., 2014)
AFastandRobustKey-FramesbasedVideoCopyDetectionUsingBSIF-RMI
41
that the pixel-wise gradient magnitude similarity
(GMS) between the reference and distorted images
combined with a pooling strategy the standard
deviation of the GMS map can predict accurately
perceptual image quality and measure efficiently the
distortion between original and distorted images.
The principle consist of convolving an image
with a linear filter such as the classic Roberts, Sobel,
Scharr and Prewitt filters and some task-specific
ones. For simplicity of computation, the Prewitt
filter is used to calculate the gradient among the 3×3
template gradient filters. Prewitt filters along
horizontal () and vertical () directions are defined
as (Xue et al., 2014):
1
3
0
1
3
1
3
0
1
3
1
3
0
1
3
,
1
3
1
3
1
3
000
1
3
1
3
1
3
,
(1)
Convolving
and
with the reference image
and distorted images yields the horizontal and
vertical gradient images. The gradient magnitude
images of and at location i, denoted by
and
are computed by small local path
in the original image or as follows:
⊗
⊗
(2)
⊗
⊗
(3)
where the symbol “⊗” denotes the convolution
operation., The gradient magnitude similarity (GMS)
map is computed as follows (Xue et al., 2014):
2
(4)
where is a positive constant that supplies
numerical stability. By applying average pooling to
the GMS map, Gradient Magnitude Similarity Mean
(GMSM) is obtained:
1
(5)
where is the total number of pixels in the image.
A higher score means a higher overall image
quality. The standard deviations of the GMS map is
computed, it is called Gradient Magnitude Similarity
Deviation (GMSD):
1
(6)
Note that the value of GMSD reflects the range of
distortion severities in an image. The higher the
GMSD score, the larger the distortion range, and
thus bigger the difference between two consecutive
frames.
The proposed key-frames extraction is based on
measuring the distortion between two consecutive
frames for the whole video sequence to detect key-
frames with significant change of the visual content.
After calculating the GMSD difference between all
the video frames sequence, a vector is obtained and
each value of the vector is compared to a threshold.
Only the fames with a distortion that exceed the
threshold value are considered as key-frames.
Figure 1: The architecture of the proposed CBVCD system.
Reference
database
Decision
Features
Matching
ReferenceVideo1
ReferenceVideo2
Referencevideo3
Key-frames
Extraction
based on
GMSD
Feature
extraction based
on fusion of BSIF
and RMI
PCA dimension
reduction
Key-frames
Extraction based on
GMSD
Feature
extraction based on
fusion of BSIF and RMI
PCA dimension
reduction
OFFLINE PROCESS
ONLINE PROCESS
Query
video
SIGMAP2014-InternationalConferenceonSignalProcessingandMultimediaApplications
42
(7)
The threshold used in key-frames extraction
process is computed using the following equation:
max
min
2
(8)
where 01, max
, min
are the
maximum and minimum values obtained when
computing the GMSD difference between
consecutive video frames, respectively.
(a)
(b)
Figure 3: Examples of frames border removal. (a) original
frames, (b) frames after border removal (samples frames
from TRECVID 2009dataset) (Liu et al., 2009).
Remark 1: The estimation of the key-frames is a
delicate task; it must be carefully selected to
minimize the video size and to be robust against
different attacks. If key-frames extraction procedure
is not robust against attacks, most of the key-frames
will changed after applying an attack resulting in
poor matching features results.
To overcome this inconvenient, we must
introduce some preprocessing on the transformed
video frames, such as border removing. As shown in
Figure 3 borders are removed by a simple method,
which removes the first few lines of each direction
(left, right, top, bottom) whose sum of intensity is
less than a threshold (20% of the maximum in this
paper).
3.2 Binarized Statistical Image
Features
BSIF was recently proposed by Kannla and Rahtu
for face recognition and texture classification
(Kannala and Rahtu, 2012). It efficiently encodes
texture information and is suitable for histogram
based representation of image regions. To
characterize the texture properties within each frame
sub-region in a video sequence, the histograms of
pixels BSIF code values are then used. A bit string
is determined from a desired number of filters where
each bit is associated with a different filter.
The set of filters is learnt from a training set of
natural image patches by maximizing the statistical
independence of the filter responses. Hence,
statistical properties of natural image patches
determine the descriptors. Given an image patch
of size pixels and a linear filter
of the same
size, the filter response is obtained by:
,
,
w
x
,
(9)
where and x are vectors which represent the
pixels of
and . By setting
1 if
0 and
0 otherwise, the binarized feature
is
obtained. For linear filters
, a matrix W of size
is stacked and all responses at once, i.e.,
s.x are computed. A bit string is obtained
by binarizing each element
of as above. Thus,
given the linear feature detectors
, computation of
the bit string is straightforward for more details;
the reader can refer to (Kannala and Rahtu, 2012).
Independent component analysis are then used to
learn the filters by maximizing the statistical
independence of
in order to obtain a set of filters
. Additionally, the independence of
provides
justification for the independent quantization of the
elements of the response vector. More details about
the training set of image patches and how to obtain
the filter matrix can be found in (Kannala and Rahtu,
2012).
3.3 Video Copy Detection
3.3.1 Feature Extraction Process
In this work, a new descriptor is introduced based on
computing the BSIF characteristics of different
images and their combination with the mean relative
intensity of each region. It is extracted by encoding
the pixel information of each key-frame.
For a given video, we segment it into key-
frames, which are the basic processing units in our
approach. Each frame
in the key-frame selection
is first converted to grayscale and resized to
128x128 pixels. A BSIF representation is then
obtained after texture information encoding. We
segmented the BSIF representation into
blocks to
construct a BSIF histogram. Next, for the th block
AFastandRobustKey-FramesbasedVideoCopyDetectionUsingBSIF-RMI
43
of a frame, the relative mean intensity (RMI) is
calculated as follows:
,/,
∈
∈
(10)
where , represents the intensity of point
,. Figure 4 shows an example for
4. The
connection between two nodes is determined by the
content proximity between two blocks. We observe
that two copies may not share common visual
properties such as colors, textures, and edges;
however, they often maintain a similar inter-block
relationship.
As defined in Eq. (10), RMI is a global feature of
each block. It represents the intra-block information
and can help maintain a similar inter-block
relationship between the query video and the
reference. Besides, it is not sensitive to some
complex brightness changes. Unlike previously
reported work, which have focused on
intensity/color variations only, the proposed
algorithm adopt a combination of BSIF and RMI to
describe the local distribution of each frame
.
BSIF is a new algorithm to face recognition used
to provide local image features (Kannala and Rahtu,
2012). For each point of a block , BSIF are
calculated and BSIF histograms of each block are
constructed (Figure 4). As can be seen, if the query
is flipped from the reference, the BSIF
representation is opposite. To avoid this change,
instead of directly using the BSIF representation, we
divide their absolute values into certain number of
bins. With the increasing of bins number
, the
discriminative power of BSIF increases. However,
the computation complexity also rises, and it will
enlarge the influence of noise. To improve the
discriminability, we combined BSIF and RMI
features into the video description.
The features combination is used as weight; it is
performed by combining the BSIF histograms to the
corresponding RMI within a block as follows:
,
,
,
,
,
,
⋮ ⋮ ⋮
,
,
⋯
,
⋮
(11)
The final descriptor D encloses the intensity and
local texture of each frame. From the calculating
process, we find that is overall a local descriptor
with the length of
. It encodes the inner
relationship of a frame and the local changes of
intensities.
For a number
of key-frames extracted from
a query video, Key-frames descriptors are
concatenated to form a matrix descriptor of size
∗
which represents the matrix feature of a
query video. The size of this matrix is then reduced
using PCA to keep only a vector of size 4
. This
means that each query video will be finally
represented by a feature vector of 4
variables.
Remark 2:
As copies may have different sizes
with the original source. Here we employ a linear
interpolation process to resize the query frames to
the same size with its reference. This process is
necessary because different sizes may cause
different forms of the descriptors. Note also that
each frame is first converted to grayscale before
resizing.
3.3.2 Matching Process
To match two descriptors
and
, we choose
Cosine distance as the similarity metric. In the
matching process, a minimization process is
employed. For an input query video, we find the
clips with the minimal distance (maximal similarity)
between descriptors in each source video. Then, we
select the one with the lowest distance in the source.
This distance is computed by the following equation
cos
.
‖
‖‖
‖
∑
∑
∑
(12)
4 EXPERIMENTAL RESULTS
4.1 Key-Frames Extraction Evaluation
A video summary should not contain too many key-
frames since the aim of the summarization process is
to allow users to quickly grasp the content of a video
sequence. For this reason, we have also evaluated
the compactness of the summary (compression
ratio). The compression ratio is computed by
dividing the number of key- frames in the summary
by the length of video sequence.
SIGMAP2014-InternationalConferenceonSignalProcessingandMultimediaApplications
44
Figure 4: Illustrations of the developed descriptor with
4,
256. (a) original frame, b) BSIF representation with
4 grids , (c) BSIF histogram of each block; (d) RMI histogram, (e) fusion of BSIF and RMI descriptor.
For a given video sequence S
t
, the compression rate
is thus defined as:
1
(13)
where
is the number of key-frames in the
summary, and
is the total number of frames in
the video sequence. Ideally, a good summary
produced by a key-frame extraction algorithm will
present both high quality measure and a high
compression ratio (i.e. small number of key-frames).
Using the developed key-frames extraction
algorithm we obtained an average compression ratio
99.6 %. From the experiment, we can see that
the representative key-frames can be extracted
accurately and semantically from long video
sequences or videos, objectively. Figure 5 shows the
results of key-frames extraction with the proposed
algorithm. The video depicts the process where
there is an abrupt change in the video sequence.
The result illustrates that the algorithm is valid to
segment the shot and extract the key-frames and it is
of good feasibility and strong robustness.
Figure 5: Example of key-frames extracted from query
video in TRECVID’09 database (TRECVID 2009).
In order to evaluate the performance of the key-
frames extraction algorithm we first used
TRECVID’09 (TRECVID 2009
)
definitions of
detection recall and precision ( and ) as shown
in Equations (14) and (18). Using the proposed
algorithm we obtained an average recall of 97.7%
and average precision of 100%.
We are currently building a larger ground truth
data set for a more thorough evaluation of our
algorithm.
(14)
where
represents the number of frames
shared between detected and reference transitions
and
represents the number of frames of
reference transitions
(15)
where
represents the number of frames of
detection transition.
4.2 Performance Evaluation Using
MUSCLE VCD
In first time, we evaluate our approach on ten
transformations. Experiments are conducted using
the CIVR’07 Copy Detection Corpus (MUSCLE
VCD) (
Law-To et al., 2007)
. The corpus used is based
on a task which consists to retrieve copies of whole
long videos (ST1). The videos used in this corpus
are size of 288352 and come from web, TV
archives and movies, and cover documentaries,
movies, sport events, TV shows and cartoons.
Meanwhile, there are 15 queries for with different
X
1 2 3 4
0
0.02
0.04
0.06
0 0.05 0. 1 0.15 0.2 0.25
0
10
20
0 0.05 0.1 0.15 0.2 0.25
0
10
20
0 0.05 0. 1 0. 15 0. 2 0.25
0
10
20
0 0.05 0.1 0.15 0.2
0
10
20
0 0.01 0.02 0.03 0.04
0
5
10
(
a
=
(
b
)
(
c
)
(
d
)
(
e
)
AFastandRobustKey-FramesbasedVideoCopyDetectionUsingBSIF-RMI
45
transformations like change of colors, blur,
recording with an angle and inserting logos.
According to the evaluation plot (Law-To et al.,
2007), criterion of the video copy detection scheme
is defined as:
Qualit
y
NCorrect
NTotal
(16)
In our simulation, we set the BSIF parameter
to be 4/9/12/16 and
to be 256 bins. The threshold
is computed using 0.75. Table 1 lists the
matching qualities with the corresponding values.
We can observe that the best results can be obtained
by using a number of grids
16. Table1 and 2
show the result obtained using CIVR’07 corpus with
the comparison to some existing approaches in the
literature in term of time execution time and
robustness to different attacks respectively.
According to the table 3, the proposed approach runs
faster than previous works.
Remark 3: As
gets larger, the descriptor will
possess more discriminate power. However, while
the dimension increases, the descriptor is more
sensitive to the border removal technique which may
lower down the overall performance.
In the other side,
Theoretically, with more BSIF bins
, more information
can be captured by the descriptor. However, for the
existence of noise, the performance will be degraded if
there are too many bins.
4.3 Performance Evaluation Using
TRECVID 2009 Dataset
The TRECVID 2009 dataset for CBCD (Liu et al.,
2009) is also used to evaluate our approach.
Table 1: Matching qualities with different blocks (n
.
Quality (%)
4 93
9 97
12 98.66
16 100
We prepare a corpus of 50 untransformed query
videos and 350 transformed queries by applying
seven classes of transformations to each query
video. The different types of transformations are
listed in Table 3. The queries last from 5 seconds to
2 minutes long.
Table 2: Transformation recognition qualities obtained
with Muscle VCD 2007.
Transformation Wu et
al.,
2009
Jiang et
al.,
2013
Proposed
AVC 89 93
95.33
Blur 88 100
100
Caption 95 100
100
Contrast 100 100
100
Crop 90 87
93
Mono 100 100
100
Noise 95 100
100
PicInPic 90 93
96.66
Ratio 100 100
100
Reduction 100 100
100
We use transformed queries to retrieve
untransformed query videos from the derived
dataset. The TRECVID 2009 dataset is challenging
because the query videos are much shorter (i.e., 81
seconds long on average) and were produced by
complicated transformations, such as, picture-in-
picture and combination of various transformations.
5 CONCLUSION
In this paper, we propose fast and robust Key-frames
for CBCD combining BSIF-RMI. RMI
characterizes a global intensity level while BSIF
represents the local distribution of the frame. The
experiments obtained by the proposed approach
show that the descriptor is effective and efficient.
The matching time has been reduced efficiently by
using only the key-frames and by reducing the
dimension of feature vectors using PCA. Promising
results are obtained for TRCVID 2009 and Muscle
VCD 2007 databases in comparison to other works
in the literature.
Table 3: Executive time of different approaches (in
seconds).
Transformation Time (sec)
Yeh and Cheng, 2009 1,394
Cui et al., 2010 849
Jiang et al., 2013 69
Proposed 32
SIGMAP2014-InternationalConferenceonSignalProcessingandMultimediaApplications
46
Table 4: List of TRECVID’09 transformations and
comparison of obtained recognition quality (TRECVID
2009).
T# Transformation
Description
Ren et al.
2012
Propose
d
T2 Picture in picture 93
96.66
T3 Insertion of pattern. 100
100
T4 Strong re-encoding. 100
100
T5 Change of gamma 100
100
T6 Three random transf.:
blur, gamma
change, frame dropping,
contrast,
compression, ratio and
noise
87
93
T8 Three random transf.:
crop,
shift, contrast, insert of
pattern,
vertical flip, picture in
picture
100
100
T1
0
Combinations of 5
transformations
chosen from T2 - T8
100
100
As a perspective, we will focus our work on the
use of all the video sequence frames in the feature
extraction task and how optimizing the execution
time to deal with real time application.
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