TEXTUAL DESCRIPTION-BASED VIDEO SUMMARIZATION FOR VIDEO BLOGS
Mayu Otani, Yuta Nakashima, Tomokazu Sato, Naokazu Yokoya
Nara Institute of Science and Technology{otani.mayu.ob9, n-yuta, tomoka-s, yokoya}@is.naist.jp
ABSTRACT
Recent popularization of camera devices, including action
cams and smartphones, enables us to record videos in everyday life and share them through the Internet. Video blog isa recent approach for sharing videos, in which users enjoy
expressing themselves in blog posts with attractive videos.
Generating such videos, however, requires users to review
vast amount of raw videos and edit them appropriately, whichkeeps users away from doing so. In this paper, we propose anovel video summarization method for helping users to createa video blog post. Unlike typical video summarization methods, the proposed method utilizes the text, which is written fora video blog post, and makes the video summary consistent
with the content of the text. For this, we perform video summarization by solving an optimization problem, in which an
objective function involves the content similarity between thesummarized video and the text. Our user study with 20 participants has demonstrated that our proposed method is suitableto create video blog posts compared with conventional methods for video summarization.
Index Terms— Video blog, video summarization, user
study
1. INTRODUCTION
Recently, camera devices, including action cams and smartphones, have spread widely, and many users enjoy recording
their everyday experiences in videos. An attractive way toshare these videos with others is authoring a video blog post,
which is a blog post with videos supporting it. Actually, many
active users have uploaded videos in their blog posts [1].
Most of such videos appear to be in the web-TV show
style, which can be easily edited from a small number of raw
videos by capturing them based on a prepared scenario in apreliminarily structured way, e.g., giving a presentation on
something. Unfortunately, it is difficult to apply this style
to most videos capturing everyday experiences; most people take videos without preliminarily preparing a scenario for
final video to be uploaded and consequently get a cluttered
set of videos. This makes video editing, especially for video
blogs, much cumbersome. A post in a video blog usually consists of text with a certain story, e.g., telling prominent events
during a user’s trip, as well as a video that may include somescenes to support the story, as shown in Fig. 1. Therefore, to

BLOG
2014/04/02?

I saw cherry trees
in blossom.
There were many
people. I fed
deer in the park.

On a warm day in March, we went to Nara Park. Before
getting to Nara Park, we went to Saho river. There werecherry trees along the river. The river is well known forcherry blossom, and many people visit during the seasonof blossom. I took many videos of other students. One of
the students, Nakashima used a special camera for his
study. He took some videos, carrying the camera along
the river. It was a beautiful place and I want to visit there
next spring again.

text

words

objectsshot selec onvideosblog post
cherrypeople cherry

deer

deer

video summaryFig. 1. Our method regards text as a bag of word objects andvideos as ones of visual objects.
generate a desirable video, the user needs to review her/his
video set and find a subset suitable for what she/he wants topresent in the video blog post.
A potential approach to alleviating this video editing process is to adopt video summarization [1]. Video summarization is a technique to produce a compact representation of avast amount of videos. However, existing methods for video
summarization are not suitable for authoring video blog posts.
Most of the methods set their goal to generate a video summary that satisfies preliminarily designed criteria such as content coverage or important/interesting events [2]. Althoughsome methods take user’s preference into account [3, 4], they
do not offer a control over the content in video summaries.
In this paper, we propose a novel method for video summarization that leverages the text in its user’s blog post for
determining the video subset to be included, considering thatthe text fully describes the user’s story, i.e., what she/he wants
to present in the video blog post. Our main contributions are
summarized as follows.
• To offer a control over the video summary, we propose a novel textual description-based framework forvideo summarization that uses text as its basis for video
subset selection. This framework is suitable for video
blogs because their posts usually consist of text andvideos, and thus the users can skip video editing process without any additional burden, since writing text
is much easier than video editing.
• To achieve textual description-based video summarization, we propose a new criterion for video shot selec-

tion, which is based on nouns in the text and objects inthe original video set as shown in Fig. 1.
• We formulate the problem of video summarization
as an optimization problem, which can be efficiently
solved by the dynamic programming algorithm.
• Our user study with 20 participants has demonstrated
the advantages of our proposed method over conventional approaches for video summarization.

Clusteringcherry: 0.2
people: 0.4

deer: 0.32
Text Representaon
I saw cherry trees
in blossom. There
were many
people. I fed deerin the park.

Shot Seleconcherry… …

2. RELATED WORKVideo summarization generates a compact representationfrom a vast amount of videos, which include diverse typesof videos such as sports videos [4], news programs [5], consumer videos [6], as well as video retrieval results [7]. Videosummarization techniques represent such videos by a set of
keyframes [3, 8] or a sequence of shots [9, 10].
Conventional video summarization methods use low-levelvisual features, such as color and motion [5, 6, 8, 9]. One major approach for video summarization is to reduce redundancy
in output summaries. For example, Gong and Liu proposeda cluster-based method in [5], which groups a set of frames
in the input videos into several clusters using low-level features and extracts keyframes closest to each cluster’s center.
Another major approach is to monitor temporal changes offeatures [8, 9]. Laganière et al. [9] extract shots where spatiotemporal features have salient changes.
A video usually contains audio signals, and some methods make use of this additional information for video summarization [10, 11, 12]. Ma et al. [11] proposed visual andaudio attention models to detect important shots in a video.
The method proposed by Taskiran et al. [10] uses the speechtranscript to achieve a video summary covering maximum semantic content.
The structures of stories and events are critical cues forcomprehensible video summaries [13, 14]. Lu and Grauman[14] suggested an observation that some objects leading toanother event are important to tell the story in a video.
Some methods utilize prior knowledge on target video
sets, e.g., by detecting a predetermined set of objects or events[4, 15, 16]. In the case of videos of certain types of sports,some specific events, such as scoring, are essential for comprehending the entire game, and thus a good video summary should cover them. Babaguchi et al. [4] defined significant events in an American football game and developed themethod to pick scenes considering a user’s preference, suchas favorite players or teams.
Some research efforts have been dedicated to take advantage of external information [4, 7, 17, 18]. Sang and Xu[17] proposed a method for movie summarization that uses
the script for the movie to retrieve characters and their dialogues. The script aligned with the movie provides shots with
important character activities. Another powerful external information is the Internet. To find preferable scenes that are

Shot Representaoinpeopledeer

Fig. 2. Overview of our video summarization method.
worth to be included in the video summary, Khosla et al. [18]trained classifiers to find them from web images.
Our method also utilizes text as external information. Being different from the existing methods, which use text forvideo content analysis, ours uses the text to determine thecontent of the output video summary so that the video summary represents the user’s story well. This is a novel framework for video summarization, because it provides users with
control over the content of the video summary in some extent.
3. TEXTUAL DESCRIPTION-BASED VIDEO
SUMMARIZATION
Our method generates a video summary based on the input
text that is to be uploaded as a video blog post together withthe video summary. Considering this purpose, a video summary must satisfy the following requirements.
1. It must contain scenes relevant to the text.
2. It must include various scenes as long as they are compatible with Requirement 1.
3. It must not be redundant.
Requirement 1 is particularly essential for video blogs, andthe others are common to most video summarization methods.
Fig. 2 shows an overview of our proposed method. Our
method takes as input a video set V , text T written by theuser, and the maximum length L of the video summary. The
proposed method first segments each unedited video in V into
short shots and annotates each of them with visual objects thatappear in the shot, considering they are essential to represent
its content. It also extracts nouns from T. The shots are thengrouped into several clusters based on the visual object annotation and side information associated with the shots such as
timestamp and geo-location. These clusters are expected toloosely correspond to the scenes that the original videos arecaptured. Using these clusters, annotated visual objects andnouns in the input text, we define an objective function thatinvolves the content similarity between the input text and aset of shots, which is then maximized using the dynamic programming. Our video summary is composed by arranging theextracted shots based on their timestamp.

Our proposed method uses clusters to set preference inorder to increase the reliability of the content similarity evaluation: For example, different instances of the same object
category may or may not suit with the input text if they arecaptured at different places and times. Suppose a user inputs text on cherry blossom on a riverside, and there are video
shots containing cherry blossom captured on the riverside andat a park. The former shot is relevant to the input text, but thelatter is not. Observing this, we set preference to all shots ineach cluster based on the content similarity between the cluster and the input text.

the frame for simplicity. More specifically, letting Ωn be theregion surrounded by the bounding box for the object on in
the frame, we define its weight value sn as∫
sn =N (x|µ, Σ)dx,(1)

3.1. Text Representation
As mentioned above, we presume that objects are essential cues for measuring the content similarity; therefore, inthis work, we represent the input text T by a set of nouns,
each of which is associated with a certain object on (n =1, 2, . . . , N ). For this, the proposed method applies speechtagging to T for extracting nouns and lemmatization to themusing [19]. We then remove predefined stop words in them,which discards words that hardly contribute to the representation of T . The input text T is represented as a vectort = (t1 , t2 , . . . , tN ), where tn = 1 indicates T contains anoun associated with the object on and tn = 0 otherwise.

3.3. Objective Function
The proposed method summarizes videos by finding a subset
S ∗ ⊂ S, which maximizes an objective function involving
the content similarity between T and S ∗ as well as the redundancy of S ∗ , i.e.,

3.2. Shot Representation
For evaluating the content similarity between the text and a setof shots, which are defined as a short video segment with consistent visual objects, the proposed method represents eachshot by a set of visual objects appearing in the shot.
The proposed method first divides each of the originalvideo in V into short shots. Considering the consistency of visual objects, we employ a video segmentation method basedon keypoint matching [20]. This method uses the number ofmatches in successive frames, which roughly indicates if thesame objects are included in these frames, as a cue for segmentation. Namely, the method divides the video if the number of matches is a local minimum.
We then annotate each shot with their visual objects withrespective bounding boxes. Based on our assumption of theconsistency of visual objects in a shot, we extract the middle frame of each shot as its keyframe and annotate it. Techniques for automatic image/video annotation based on general object detection, e.g., a method proposed in [21], can beapplied. In our framework, since we have a list of possible objects in the video summary as t, it might be possible to train
object detectors for them automatically using the Internet images [22]. In this paper, however, we manually annotate theshots to demonstrate the potential performance of our methodwith perfect annotation and bounding boxes.
The proposed method then computes a weight value foreach visual object, which can be deemed as the importance ofthe visual object. Various methods can be used for measuring
the importance such as in [23]; in this work, we set the weightvalue solely based on the position and size of the object inx∈Ωnwhere N represents the Gaussian with the mean µ being
the frame center position and the predefined variance Σ.
The i-th shot is denoted by a vector of weight values si =(si,1 , . . . , si,N ), and the entire video set by S = {si |i =1, . . . , I} where I is the number of shots in V .

f (S, t) = Sim(S, t) + βCvrg(S, t).

(2)

The first term represents the content similarity roughly corresponding to Requirement 1, and the second term the coverageof content related to the scenes described in the text, corresponding to Requirement 2. The redundancy of S ∗ stated in
Requirement 3 is indirectly incorporated into the second term.
This objective function can be approximately maximized using the dynamic programing algorithm [24]. The following
sections detail our objective function.
3.3.1. Clustering-based Shot Preference
The content similarity solely based on each shot is not reliable; therefore, we introduce the preference for each shotbased on the scene, in which it is recorded. We group theshots into several clusters, each of which corresponds to acertain scene, and calculate the similarity between the objects
in each clusters and the input text T as the preference.
For clustering the shots, we adopt affinity propagation[25] with the following criterion for i ̸= j.
[]
λ min(|τi − τj |, θ)A(si , sj ) = exp −+ γJ(si , sj ), (3)Mwhere τi denotes the temporal frame index of the keyframe
for the i-th shot, M the number of frames in S, and J(·, ·) the
weighted Jaccard similarity defined as∑min(si,n , sj,n )J(si , sj ) = ∑ n
.
(4)
max(s
i,n , sj,n )nλ, θ, and γ in Eq. (3) are parameters. We set A(si , si ) to themedian of A(si , sj ). The k-th cluster is represented by ck =(ck,1 , ck,2 , . . . , ck,N ) where ck,n = 1 means on is included init (i.e., the cluster contains at least one shot that gives si,n >0) and ck,n = 0 otherwise. The preference pi for the i-th shotis defined as pi = J(Ci , t) where Ci is the cluster to which
the i-th shot belongs. The preference pi is high when Ci has
similar content as t, which is used for defining Sim(S, t) andCvrg(S, t).

On a warm day in March, we went to Nara Park. Before getting to Nara
Park, we went to Saho river. There were cherry trees along the river. The
river is well known for cherry blossom, and many people visit during theseason of blossom. I took many videos of other students. One of thestudents, Nakashima used a special camera for his study. He took somevideos, carrying the camera along the river. It was a beautiful place and I
want to visit there next spring again.
We went to Nara Park. A lot of deer were around the Nandaimon. There
were also a few cracker shops, and many tourists enjoyed feeding deer. Ibought some crackers and deer immediately gathered around me.
Nandaimon is a famous gate in the Nara Park. I saw a statue of Nandaimon. There were many people.

Fig. 3. Texts used in experiment. From top to bottom: Text 1,
Text 2, and Text 3.
3.3.2. Content Similarity
Intuitively, a set of shots S and the input text T should havehigh content similarity when they share many objects. Basedon this observation, we define the content similarity as
Sim(S, t) = J(ϕ(S), t),(5)where ϕ(S) is the sum of all si in S weighted by pi , i.e.,∑ϕ(S) =
pi si .
(6)si ∈S

This term encourages including shots that contain visual objects referred in T . When the shared objects have large
weights or the preference of a shot is high, the shot is more
likely to be included. It also penalizes shots with visual objects that are already included enough because the content
similarity decreases when weight values of the visual objects
exceed those of the text.
3.3.3. Content Coverage
For Requirement 2, we need higher coverage of the content
related to the scenes in T , which indirectly encourages to reduce the redundancy of the video summary S ∗ as well by allowing visual objects that are not in T but in the clusters withhigh similarity to T , considering Requirement 1. By this, we
encode our observation that inclusion of visual objects thatappear in scenes relevant to T provides a more complete senseof the surrounding situation during video capturing, even if
the visual objects are not included in T . Content coverage isthus defined as
Cvrg(S, t) = J(ϕ(S), ψ(t)),(7)

where ψ(t) gives the set of visual objects that appear in theclusters relevant to T , i.e., the n-th element of ψ(t) is 1 when
object oi is included in clusters {ck |J(ck , t) ≥ ρ}, where ρ
is a predetermined threshold.
4. EXPERIMENTAL RESULTS
We evaluated the proposed method by user study with 20 participants to verify the following points.

Table 1. Input text and methods to be evaluated.
(a)
(b)
(c)
(d)
(e)
(f)
(g)
(h)
(i)
(j)
(k)

Input
No text
No text
Text 1
Text 1
Text 1
Text 2
Text 2
Text 2
Text 3
Text 3
Text 3

MethodUniform sampling.
Cluster-based.
Ours.
Description-based w/o content coverage.
Description-based w/o content coverage and preference.
Ours.
Description-based w/o content coverage.
Description-based w/o content coverage and preference.
Ours.
Description-based w/o content coverage.
Description-based w/o content coverage and preference.

1. Our objective function is designed to make a video
summary similar to the input text as well as to include
various objects indirectly related to it. We confirm if
this objective function is suitable for video blogs.
2. We verify if the objective function, which encodes theredundancy and coverage criteria, actually works tomake the content of video summaries similar to the input text.
In our evaluation, we used a video set containing 42
videos, which are 80 min in total, capturing a short trip ina day. The video set includes various scenes in, e.g., a car, ariverside, a park. Fig. 3 shows the three paragraphs, each ofwhich can be deemed as text for a blog post. We generated
video summaries based on each input text with L = 20 sec,and each output by the proposed method is shown in Fig. 4.
The parameters were empirically set to Σ = diag(8w, 8h),where w and h are the width and the height of the frame,
β = 0.25, λ = 5, θ = 36000, γ = 0.25, and ρ = 0.1.
To clarify the advantage of the proposed method, we compare it with several baselines (Table 1). (a) is generated by
uniform sampling that results in a naive summary including 10 shots sampled at uniform intervals. (b) is a clusterbased summary, which includes exemplar shots of the clusters derived in Sec. 3.3.1. The shots are selected to contain as
many visual objects as possible in the video summary. Thesebaselines represent summaries without consideration of user’s
story. In order to investigate the performance of our textual
description-based method with Text1, Text2, and Text3 ((c),(f), and (i)), we also compared it to some variants of ours. In(d), (g), and (j), the second term Cvrg(S, t) is turned off, andin (e), (h), and (k), the preference in Sec. 3.3.1 was ignored
as well. They were generated so that each of them was about
20 sec long.
4.1. Suitability to Video Blog Post
For evaluating the suitability of the our video summarization
method to video blog posts, our subjects watched 11 video
summaries generated by methods in Table 1. They also reviewed a video blog post as shown in Fig. 5. They were thenasked to score each video in terms of how well the video suitswith the blog post. The score ranges from 1 to 5, where 1means that the video definitely does not suit with the blog
post, and 5 means that it suits very well. The subjects wereText1

BLOG
2014/04/02

On a warm day in March, we went to Nara Park. Before
getting to Nara Park, we went to Saho river. There werecherry trees along the river. The river is well known forcherry blossom, and many people visit during the seasonof blossom. I took many videos of other students. One of
the students, Nakashima used a special camera for his
study. He took some videos, carrying the camera along
the river. It was a beautiful place and I want to visit there
next spring again.

Text2

Text3

Fig. 4. Keyframes of shots in the video summaries by the proposed method.

Fig. 5. An example video blog post
shown to subjects in user study.

5

5

5

4

4

4

3

3

3

2

2

2

1

1

1

0

0

0

(a) (b) (c) (d) (e) (f) (g) (h) (i) (j) (k)(a) (b) (c) (d) (e) (f) (g) (h) (i) (j) (k)(a) (b) (c) (d) (e) (f) (g) (h) (i) (j) (k)Text 1
Text 2
Text 3Fig. 6. Average scores on suitability to video blog post with Text 1, Text 2, and Text 3. The methods in Table 1 were evaluated.
Video summaries that take the same input text as each video blog post are colored in green.
divided into three groups, and a video blog post with different text in Fig. 3 was assigned to each group to evaluate thesubjects’ responses to different input text.
Fig. 6 shows the results for Text 1, Text 2, and Text 3,
which demonstrate that the proposed method got positive responses for Text 2 and Text 3. For Text 1, the video summary by clustering (b) outperformed ours. This is because thecluster-based video summary (b) includes many shots related
to the Text 1 by chance. The summary (b) has another advantage for Text 1. The summary (b) includes shots showing thepeople heading where the scene in Text 1 was captured. Theseshots are regarded as leading shots proposed in [14], whichsmoothly lead to the next scene and improve comprehensibility. Although inclusion of leading shots is not designed in thecluster-based video summary (b), they caused the high score
for Text 1. These results for Text 1, Text 2, and Text 3 also
show the effects of the content coverage and the preference
based on clustering, i.e., content coverage does not affect thescore much, but the preference gives significant improvementof suitability. In conclusion, the subjects basically preferred
our method for video blogs to other methods, but inclusion ofleading shots may improve the suitability.
4.2. Verification of Objective Function
To verify our objective function, we asked the subjects towatch a video containing middle two seconds of all input

videos in our video set and to score each method in termsof the following three aspects:i). How well the video represents the input text (similarity
between text and video summary).
ii). How redundant the video is.
iii). How well the video covers the content of the entire
videos.
Figs. 7 (i)–(iii) show the results. As for (i), the result indicates our summaries gain the highest average scores for corresponding input text, which means that our objective function
sufficiently works to include the content similar to the input
text. In terms of (ii) redundancy and (iii) content coverage,
our method got a lower score than others. On the redundancy,
although the proposed method tries to reduce the redundancy,
it still uses multiple shots from similar scenes because thecandidate shots are restricted to ones relevant to the content
of the text. On the content coverage, this result is expected
because our method does not cover the entire video set but
only a subset determined by the input text.
5. CONCLUSION
In this paper, we have proposed a novel method for video
summarization that uses textual description to control the content in resulting videos. The proposed method suits to generating video for video blog posts. We have designed an
objective function that encodes our observation that a good

5

5

5

4

4

4

3

3

3

2

2

2

1

1

1

0

(a) (b) (c) (d) (e) (f) (g) (h) (i) (j) (k)(i) Simirality to the text0(a) (b) (c) (d) (e) (f) (g) (h) (i) (j) (k)(ii) Redundancy0(a) (b) (c) (d) (e) (f) (g) (h) (i) (j) (k)(iii) Coverage of the entire content

Fig. 7. Average scores for questions (i) to (iii). In (i), yellow is for the Text 1, green for the Text 2, and blue for the Text 3.
video summary contains similar content to the text in the blog
post. The user study successfully demonstrated that our proposed method is advantageous over the conventional methods
in terms of suitability to video blogs. Our future work includes investigation of preferred transition effects for video
summarization. Another interesting research direction is tofacilitate object detection techniques for automatically annotating original videos with leveraging the input text.
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