KDDI LABS AND SRI INTERNATIONAL AT TRECVID 2010:
CONTENT-BASED COPY DETECTION
Yusuke Uchida, Shigeyuki Sakazawa

Motilal Agrawal, Murat AkbacakKDDI R&D Laboratories Inc.
2-1-15 Ohara, Fujimino-shi
Saitama, JapanSRI International
333 Ravenswood Avenue
Menlo Park, CA, USA

ABSTRACTWe describe our system for Content-Based Copy Detection(CBCD) task submitted to TRECVID 2010. Our system ismulti-modal and integrates the results of global visual features, local visual features and audio features to produce thefinal run results. Each of these features is designed to takecare of different aspects of the video transformations. We
submitted two runs each for BALANCED as well as NOFA
profile:Global-based CBCD

Local-based CBCD

Reference ID
Timestamp offset
Total score
Frame-level score

Multimodal integration

Query
Audio-based CBCD

Fig. 1: Framework of our CBCD system.

• KDDILabs-SRI.m.balanced.1• KDDILabs-SRI.m.balanced.2• KDDILabs-SRI.m.nofa.1• KDDILabs-SRI.m.nofa.2
These runs all use the same CBCD framework for each of thethree modalities and differ only in the parameters for the final
integration step. Our CBCD framework has made significant
advances in video based copy detection. We introduce novel
algorithms to obtain robust results against various transformations: dense-sampling-based global SIFT features, improved
indexing methods for both global and local features and handling temporal burstiness. TRECVID 2010 evaluation results
show that our system achieves good performance for both detection accuracy especially on NOFA profile and localization
accuracy.
1. INTRODUCTION
In recent years, Content-Based Copy Detection (CBCD) technology, more generally referred to as digital fingerprinting,has attracted considerable research attention. There are many
applications of CBCD technology such as detecting copyright
infringement on video sharing sites, content identification andtracking, or query-by-example searches.
Given a test collection of videos and a set of queries, thegoal of the CBCD task in TRECVID is to determine for eachquery the place, if any, that some part of the query occurs,with possible transformations, in the test collection.

Figure 1 gives an overview of our CBCD system. Our system runs global, local and audio based CBCD simultaneously
for each query. Each of the three modes produces results independently in the form of a reference video identifier, corresponding timestamp offset, total score and frame-level scores.
The results of each of the modalities are integrated in a postprocessing stage to make the results reliable and accurate. We
fuse these results using a voting scheme that emphasizes results where at least two of the three modes are consistent.
The basic idea behind using multiple modalities for
CBCD task stems from the fact that each of these modes
work well for different transformations that are in some sensecomplementary. While it is apparent that audio and video
have completely different and complementary properties, it isalso easy to see that global and local features have different
characteristicts: local features are robust even for geometrictransformations (T1, T2, T8, T10), while global features are
more robust for non-geometric (photometric) transformations(T4, T5, T6). In addition, global features are much fasterto compute and will work well for all transformations withsmall or no-change in geometry. Our audio features are simple and fast although we believe that their performance could
be greatly enhanced by a tighter integration with video anduse of more robust audio features.
In the following sections, we describe each of the threemodalities in some detail and finally discuss the integration
framework and evaluation results.

QueryReference

Frame sampling

Frame sampling

Rectangular
window detectionDense feature extraction
with varying scales

Dense feature extraction(40 locations)with varying scales

Feature location

Window for 2nd word
Window for 1st word
Image frame

Bigram signatures

Bigram signatures

Fig. 4: The two nested windows for making a bigram feature descriptor at a given feature location and scale.

Filter burstiness
Geometric mapping

Inverted index

Global results

Fig. 2: Overview of global feature based CBCD.

Image frameFig. 3: Two neighboring windows corresponding to a scale of 1/3
the width and height. The feature locations (shown as circles) aresuch that the windows overlap 75% between the neighbors.

2. GLOBAL FEATURE BASED DETECTION

Figure 2 illustrates a functional diagram of our global feature
based CBCD scheme. Our global feature based detection relies on dense sampling of the image at fixed locations. Our
global features are obtained by densely sampling the frame atmultiple fixed locations in the image. The scale of the features dictates the window size to be used for computing thedescriptor at that feature location. We have used scales that
correspond to window sizes greater than or equal to one third
the total width or height.
For a given scale, feature locations are chosen such thatthe neighbors in the x and y directions overlap at least 75%.
This results in a total of 121 windows although our approachfor TRECVID only used a subset of 40 such windows. Figure 3 shows two such neighboring feature locations for a scaleof 1/3 the width and height of the image. In comparison to
local features, we do not have to perform feature detection
and scale selection of the selected features, thereby making it
faster. As discussed earlier, global features however are lessrobust to drastic changes in geometry.

2.1. Bigrams as feature descriptors
Conventional global descriptors such as rank-based features[1, 2] are not distinctive enough in our dense sampling strategy. SIFT descriptors [3] on the other hand are known toperform well. We used bigrams of SIFT descriptors to makeit more distinctive.
Given a feature location and scale, we extract two 128 dimensional SIFT descriptors from the window corresponding
to that scale and use the bigrams as the descriptor for that feature location and scale. The first descriptor is extracted fromthe full window and the second descriptor is extracted froma sub-window centered on the feature location with a widthand height half of that of the full window. We quantize eachof the two descriptors into 10,000 words independently andthe global descriptor for that feature location and scale is thenrepresented by their bi-grams. For a vocabulary size of 10,000
for each of the words, this results in a bigram vocabulary sizeof 100 Million. We have found that using the bigram resultsin much better performance and also results in faster execution time. Figure 4 shows the two nested windows for bigramcomputation of a feature location and scale.
2.2. Indexing with multiple assignments
We use the bigrams to make an inverted index and index eachframe of the reference video. Use of bigrams results in a large
vocabulary size but in order to increase the recall, we use
multiple assignments for indexing. We have found that using
multiple assignments in both query and reference side helps
improve the recall. Each descriptor in the bigram is assigned
to five words. Therefore each bigram in both the referenceand query side gets assigned to 25 words.
2.3. Automatic geometric correspondence
On the query side, global features are computed in a manner
similar to the reference video. When querying, for each feature location in the query video, its correspondence is withthe same feature location and scale in the reference video.
Therefore we do not have to perform any matching step of
the feature locations. In order to make the approach robustReference frame

Query frameFig. 6: Examples of detected PIP window.

Fig. 5: Automatic correspondence determination based on positionand scale. The green features are corresponding locations. The redfeature locations are neighbors in the reference frame and are included to make the matching robust to small shifts.

to small shifts we also match it with its four neighbors in thereference video with the same scale.
Figure 5 demonstrates these 4 neighbors in the reference
video.

Reference frame

Query frameFig. 7: Geometric correspondence for the PIP transform. Matchingis performed relative to the detected window.

2.4. Special case: Picture-in-Picture detection
Since our global features are not scale invariant, they would
not work well for cases where the frame undergoes drasticchanges in scale. Therefore our approach described so far
will not work well for the picture-in-picture (PIP) transformation. Our approach detects picture-in-picture and patterninsertion transformations by detecting rectangular windows
in the query video. Windows are detected by accumulating
image gradients in the x and y directions over time. We runedge detection in each video frame with a very low threshold and then accumulate the number of edges in each row
and column temporally. Rows and columns with sufficientnumber of accumulated edges are candidates for the edges of
the window. The intersection of a pair of row and columnedges is a candidate corner for the rectangular window. Finally, those windows with sufficient number of edges in atleast two of the four sides are the candidate detected windows. In our experiments, we have set thresholds such that
our system hardly misses any such window. Of course, thismeans that sometimes it detects a lot more windows than actually present. Figure 6 show the detected PIP windows in a
few frames.
If a rectangular window is detected in the query window(for a PIP transformation), the feature locations and scale aredetermined relative to the detected rectangular region insteadof the whole frame. This is illustrated in Figure 7. When a PIP
window is detected, querying is performed with the detected
window in addition to the whole frame. This takes care offalse PIP detections.
2.5. Querying
For each query frame, we use the 40 global features to finda corresponding match in the reference frames. The score ofa match is simply the count of the number of global feature

locations matched. Each such match results in a vote for thetime offset corresponding to that match. The offset that getsthe most votes is taken as the best match for that query.
If two consecutive frames are similar, they do not add new
information for matching. This is the problem of temporal
burstiness. If not accounted for, this will result in a lot ofincorrect offsets. Temporal burstiness is easy to handle in oursystem. We compare the bigram from two consecutive frames
and if either of the two words are the same, we do not use thatframe for our matching. We skip all frames similar to thelast good frame, until we find a frame with bigrams that are
different from the last good frame.
2.6. Frame sampling
For both global and local features, each video is resampled
at a fixed frame rate to extract key frames. This makes ourreference database size tractable and also helps us deal withframe rate changes. In our submission, we sampled at 2.0 fps
on both the query and reference side. For the reference set of
approximately 400 hours, this produces 400×60×60×2≃3M(key)frames.
3. LOCAL FEATURE BASED DETECTION
Figure 8 illustrates a functional diagram of our local feature
based CBCD scheme. We use local scale invariant SIFT
features and their bag-of-features representations, which has
been used widely in image/video retrieval areas [4–7]. The
key idea here is (1) the use of USIFT feature descriptor for
distinctiveness, (2) a novel variant of product quantization
based indexing for more accurate nearest neighbor searchesand (3) considering the temporal burstiness of local featuresin scoring to suppress false alarms.

QueryReference

Frame sampling

Frame sampling

USIFT extraction

Coarse quantization
Vector decomposition
Reference vector
Residual vector
Residual sub-vectors
Cell ID Switch codebooks

USIFT extraction

Cell ID

Product quantization

Tracking
Index search
Offset-level integration

Query vector

Inverted index
lookupResidual vector

Product quantization
14 34 19

2

Distance estimation(and filtering)
Residual sub-vectors

Inverted index

Geometric re-ranking

Fig. 9: Modified product quantization based indexing.

Local results

Fig. 8: Overview of local feature based CBCD.

3.1. USIFT extraction
For each keyframe, we extract the SIFT features and use upright SIFT (USIFT) as the feature descriptor. USIFT is more
distinctive and faster to compute than SIFT [8]. Since noneof the video transformations include a large rotation, USIFT
can be used without degrading the robustness of the CBCD
system. Each key frame is then represented by a set of features points (bag-of-features). Each feature point contains thefollowing information: video identifier id (only for reference
video frame), timestamp ts, position of feature point (x, y),and USIFT feature vector f .
3.2. Product quantization based indexing
We use a novel version of the product quantization based
method [9] to index USIFT feature vectors obtained in the
USIFT extraction step. The product quantization based
scheme can be integrated with an inverted index, referred
to as IVFADC in [9]. A reference vector is first quantized bya coarse quantizer with the size of N (50K in our submission),then the residual vector from the corresponding centroid is
encoded by product quantization into a short code.
In our CBCD system, we modify the original productquantization based method to use an arbitrary number ofcodebooks in product quantization, while a residual subvector is quantized by a single codebook in the originalalgorithm. The framework of our product quantization based
indexing method is shown in Fig. 9. In the indexing step,
codebooks used in the product quantization are switched according to the cell that the input vector is quantized into in
the coarse quantization. Each cell (centroid) in the coarse
quantizer has pointers1 to the codebook by which each vector
assigned to that cell should be quantized in product quantization. An arbitrary number (M ) of codebooks for productquantization are created with the following procedure:
1. For each n = 1, · · · , N , create a set of residual sub1 Actually, there are the same number of pointers as decomposed subvectors (=8 in our submission).

vector Rn from all vectors assigned to the n-th cell ina coarse quantizer and assigned it to a random clusterm ∈ {1, · · · , M }.
2. For each m = 1, · · · , M , update codebook Cm by clustering all residual sub-vectors assigned to cluster m.
3. For each n = 1, · · · , N , assign Rn to the cluster m̂, s.t.
the codebook Cm̂ achieves minimum error in quantization of Rn .
4. Repeat Step 2 and Step 3 until convergence.
In the case of M = 1, our codebook becomes identical to thecodebook used in [9]. Use of larger M reduces the quantization error in product quantization and improves the nearest
neighbor search accuracy at the cost of an additional memory required. In our submission, residual vectors are decomposed into 8 sub-vectors and the number of codebooks M
used in product quantization is set to 256. In this case, only
256×256×128≃8M byte memory is required to store codebooks.
3.3. Index search
In the search step, USIFT features in a query video are efficiently matched with reference features using an inverted index [4]. In addition, we can filter out many false matches
through distance estimation in product quantization [9]. In
our submission, we filter out reference features with a distance less than 0.3 (our SIFT features are normalized to anorm of 1.0). The result of the index search step is a set ofmatched keypoint pairs (Q, R), where each query keypoint
Q has timestamp tsq and coordinate (xq , yq ), and each reference keypoint R has video identifier id, timestamp tsr andcoordinate (xr , yr ).
3.4. Offset-level integration
Feature-level results obtained in the index search step are integrated into offset-level results using a voting scheme [7,10].
Every matched keypoint pair (Q, R) votes to the corresponding bin b[id][tsr − tsq ] in the 2D Hough space. After performing non-maxima suppression and thresholding, we obtain the top 200 hypothesis represented by (id, offset), whereoffset = tsr − tsq . Each hypothesis (id, offset) has a listof matched keypoint pairs that have voted for the hypothesis, and this information is used for geometric verification.
We also tried to incorporate the weak geometric consistency(WGC) method [6] using scale information 2 , but it did notcontribute accuracy in our preliminary experiments. This isprobably because WGC based on scale is not as useful as one
based on the orientation shown in [11].
3.5. Keypoint tracking
In the tracking step, query keypoints are tracked against keypoints in one and two previous frames. Then, each query keypoint Q has a list of keypoints Q′ s.t. 0 < tsq − ts′q ≤ 2,(xq − x′q )2 + (yq − yq′ )2 < r2 and ||fq − fq′ ||2 < th. The
lists are used in the geometric re-ranking step to alleviate thetemporal burstiness effect.

Reference

Reference

t-2
Query

t-1

t

t-2
Query

t-1

t

t’-2

t’-1

t’

Same keypoints with different time stamps
t’-2

t’-1

(a) Bursty caset’(b) Non-bursty caseFig. 10: An example of the temporal burstiness effect. (a) The samefeature is matched on successive frames. This might occur even between irrelevant videos especially in stationary scenes. (b) Different
features are matched on successive frames. This rarely happen between irrelevant videos.

3.6. Geometric re-ranking
Finally, we perform geometric verification and re-rank the result obtained in the offset-level integration step. For eachresult, we estimate the transformation matrix with 4 dof [5]between the query video and the reference video using the
RANSAC algorithm and update the score by the maximum
number of inliers. At the same time, temporal burstiness istaken into consideration in a similar way to [12,13]. The basic
idea is to suppress bursty false matches that frequently appear
in static scenes. For example, we try to put more emphasis on
the right case in Fig. 10 than the left case where the same keypoint is matched sequentially. If the same keypoint matched
K times in different frames, we√ tried three scoring strategies
for this situation: (1) K, (2) K, and (3) 1. Apparently, (1)produces the same score as the conventional voting scheme
does. In our experiment, case (3), which seems to be the mostextreme case, achieved the best performance, and we adopted
this scoring strategy in our submission. Now, we obtain a listof results based on local features. Each result includes the reference video identifier, corresponding timestamp offset, total
score and frame-level scores as described in Section 1.
4. AUDIO FEATURE BASED DETECTION
For audio features, we have used binary filterbank energy differences [14] calculated as follows:{
1 if EB (n, m) − EB (n, m + 1) > 0
F (n, m) =(1)0 otherwise,

Fig. 11: Filterbank energy in the mel scale.

10 msec. Our raw filterbank energy features are 23 dimensional. Filterbanks are placed using a Mel-scale as shown in
Fig. 11. However, in our evaluation we have found that thehigher bands of this feature are noisy and decrease the performance of the system. Therefore, we are using only the lower
15 bands for this feature. This corresponds to a band limit of5K.
For binary filterbank energy, we used hashing scheme tomake an inverted index [15]. In addition to using the 14 consecutive filterbank energy differences, we are also using 8
non-consecutive filter bank differences to make a 22 dimensional hash code. We have implemented multiple assignments
for hashing (up to 32). Our hashing scheme is very fast anddoes not require quantization steps. Figure 12 shows the useof the first 15 bands from the 23 dimensional feature vector for hashing based indexing. The figure shows two nonconsecutive filterbanks that are part of the 8 non consecutive
hash codes. The search step includes the same voting scheme
described in the local and global parts.

5KHz

F (n,1) F (n,2) …

where EB (n, m) denotes the energy in m-th filterbank forthe n-th frame. Frame size is 25.6 msec, and the skip rate is
2 Orientations of matched keypoint pairs are always consistent in the
USIFT-based scheme.

F (n,13) F (n,14)

EBn,24EBn,1
F (n,15) F (n,16)…

F (n,21) F (n,22)

Fig. 12: Binary filterbank energy feature extraction.

which results in the trivial solution i = j = arg maxt ft .

Query frame N

Query frame 1

Global scores+

6. CBCD EVALUATION RESULTS

Local scores+
Audio scores=

Integrated scores f1

fi

fj

fN

Fig. 13: Frame-level score integration.

5. MULTI-MODAL INTEGRATION
Each of the above three modalities produces a sorted listof results consisting of the reference video identifier, corresponding timestamp offset, total score and frame-level scores.
These results are sorted by the total scores with the best result
having the highest total scores. These individual results areintegrated to produce a final list of results.
5.1. Integration and re-scoringFirst, for each modality, all total scores are normalized by thesecond top score to emphasize distinctiveness. Then, any twoor three results out of the three modalities that indicate thesame id and oﬀset are integrated by simply summing the total
scores. In addition, a result that has all three modes consistently receives an additional score of 5.0 (10.0) for the BALANCED (NOFA) profile, and a result that has two out of threeconsensus points receives an additional score of 3.0 (5.0) for
the BALANCED (NOFA) profile. Frame-level scores are also
integrated frame-by-frame after they are normalized so thatthe sum of the frame-level scores becomes 1.0 as shown in
Fig. 13. We have found that this normalization step greatly
improved the accuracy of the segment localization.
5.2. Segment localization
Finally, start frame î and end frame ĵ of the copied segmentin the query video is determined byarg max Si,j .
i,j

(2)

Si,j is the partial sum of frame-level scores from frame i to jnormalized by the segment length:∑j
t=i ft√Si,j =,(3)
j−i+1+α+βwhere f1 , · · · , fT indicate frame-level scores. Although it issolved in a brute-force manner, an integral image can drastically accelerate the computation. The equation somewhat
includes heuristics trying to penalize any too short segmentsfrom the equation∑j
t=i ft,(4)
arg maxi,j j − i + 1

Figure 14 shows the evaluation results of our run KDDILabsSRI.m.nofa.1 for the NOFA profile. We found that our results based on the actual threshold (left) were not as good as
the optimal results (right) in terms of NDCR. This might bebecause we determined our threshold using very small subset (20 hours) of the previous year’s dataset. Instead, someof our results seem to have achieved the best performancein terms of optimal NDCR. This comes from our improvement in both global and local indexing and especially froman integration scheme where scores depends almost only on aconsensus among modalities and distinctiveness, not on absolute voting scores. In Fig. 14, it is also clear that our scheme
has achieved almost the best performance in segment localization criteria. This is because, in our framework, copied
segments are localized in the integration step, not in each audio or video detection framework. Score normalization prior
to localization might also contribute to the performance. In
terms of computational cost, our scheme requires almost thesame cost as the median for all participants. The bottleneck ofour current implementation is quantization of query features,
where features are quantized by a flat quantizer. This quantizer should be replaced by more efficient quantizers [5,6,16].
Figure 15 shows the evaluation results of our run KDDILabsSRI.m.balanced.1 for BALANCED profile.
7. CONCLUSION
The system is based on global SIFT, local SIFT and audio features and efficient indexing methods. The evaluation results
show that our system achieves good performance for both detection accuracy, especially in the NOFA profile and localization accuracy. Future work will include tunings and validation
in a larger dataset.
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Fig. 15: Our actual (left) and optimal (right) results for BALANCED profile.

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