Automatic Construction of Action Datasets
using Web videos with Density-based Cluster
Analysis and Outlier Detection
Nga Hang Do and Keiji Yanai
The University of Electro-Communications
185-8585 , Japan Tokyo Chofu Chofugaoka 1-5-1

Abstract. In this paper, we introduce a fully automatic approach toconstruct action datasets from noisy Web video search results. The idea
is based on combining cluster structure analysis and density-based outlier detection. For a specific action concept, first, we download its Web
top search videos and segment them into video shots. We then organize these shots into subsets using density-based hierarchy clustering.
For each set, we rank its shots by their outlier degrees which are determined as their isolatedness with respect to their surroundings. Finally,
we collect upper ranked shots as training data for the action concept. We
demonstrate that with action models trained by our data, we can obtainpromising precision rates in the task of action classification while oﬀering
the advantage of a fully automatic, scalable learning. Experiment results
on UCF11, a challenging action dataset, show the eﬀectiveness of our
method.
Keywords: automatic construction, action datasets, Web videos, densitybased clustering, outlier detection

1

Introduction

High quality datasets play important roles in computer vision and pattern recognition tasks. With suﬃcient and high quality training data, most pattern recognition methods have achieved promising results. As data sources, most recently
released datasets exploit Web data which are extremely numerous and easy toobtain. However, since Web data are generated and uploaded by general users,
data corresponding to the concept of interest account for only a small proportion
among retrieved results. Therefore, constructing high quality datasets with Webdata requires extensive human eﬀort of manual annotation. In case of constructing action datasets, in general, we need annotators to localize relevant video
parts (shots) of the pre-defined actions in video sources by watching the wholeof them carefully. Since the task is too exhausted, even largest action datasets
cover not more than 101 concepts with only several thousands of video shots.
This situation has given rise to the need for constructing action datasets withless human eﬀort.
Previous work which aim to automatically obtain action shots of specificaction concepts from noisy data [1–3] generally require textual information provided together with videos such as movie script [1] or metadata (tags) [2, 3].
Laptev et al [1, 4, 5] proposed methods to automatically associate movie scripts
with actions and obtain video shots in movie representing particular classes of2

Nga Hang Do and Keiji Yanai

human actions. Their methods actually can help reduce human eﬀort on construction of realistic action database. However, targeted videos are only themovies with available scripts and trainable actions are limited to only actions
appeared in movies. On the other hand, our proposed system can be applied to
extract data for various types of actions which are distributed over much moreimmense video source.
As an approach which also targets on more various actions and use broader
data sources, Nga et al. [6, 2, 7] proposed to collect video shots corresponding to
any kind of action concept using Web videos. They conducted experiments formore than 100 action concepts and obtained promising results. Their work is themost related work to ours and treated as our baseline in this paper. According
to their methods, before video downloading, videos are ranked based on usage
frequencies of tags. Videos which have tags with high co-occurrence frequencies
are considered as relevant videos. Therefore, their approach cannot make use ofvideos without associated tags. In this work, we propose an approach which also
exploits videos without tags. As visual features, we extract temporal featuresusing a ConvNet trained on UCF-101 dataset [8] following a recent state-of-theart approach for action recognition [9].
In action recognition, in almost cases, a primary action is considered as atarget in both training videos and test videos. Even with only one action, thetask is still challenging due to variability of human actions. The actions canlook diﬀerent when they are seen from diﬀerent views or operated by diﬀerent
people. They even can be manipulated in many disparate ways. Thus, to obtain
good recognition performance, training data should capture actions in many
diﬀerent conditions. In other words, a high quality action database should reflect
as much as possible the diversity of the concept. However, previous approaches
for automatic construction of action database do not cope with concept diversity.
Especially, our baseline [6] applies VisualRank [10] which is originally an image
ranking method with a visual feature based similarity matrix to rank shots. Shots
sharing the most visual characteristics with others are ranked to the top andselected as relevant shots. Therefore, this method tends to obtain only visuallysimilar shots. In this paper, we propose to group related shots into clusters
before shot ranking by a hierarchy clustering method [11]. Diﬀerent clusters while
sharing some appearance characteristics still hold unique aspects of the concept.
Consequently, our obtained shots are much more diverse than shots obtainedby the baseline [6]. According to our experiment results, the more diverse thetraining data are, the better recognition performance we can achieve.
After obtaining clusters, we rank instances in each cluster by outlier factor [12]. Outliers are instances deviating from the major distribution of the data.
In other words, outliers belong to sparse regions while relevant instances lie in
dense regions. The most densely linked instances from each cluster are ranked
to the top and then used as training data for the concept. As action concepts,
we experiment on those used in YouTube (also called as UCF11) dataset [13].
Furthermore, we train action models with our automatically collected data andtest them on the test data of these datasets. We performed action classification
by a popular supervised framework with the intention of comparing classification
performance using manually constructed dataset and the dataset collected automatically by our proposed approach. Experiment results show that even though
our data are not qualified as “clean” data as standard training data (manually
collected data), classification rates are promising and show potential for develop-

Automatic Action Dataset Construction from Web

3

ment of approaches for automatic construction of action databases. Our work is
inspired by [14] which uses density analysis of Web images for automatic image
dataset construction.
Our contributions can be summarized as follows: (1) We propose a simple
yet eﬀective and feasible approach for fully automatic construction of action
datasets; (2) We address intra-class variations within a concept resulting in multiple groups of shots; (3) We validate our automatically constructed datasetson standard datasets to show the potential of automatic construction for action
datasets. To the best of our knowledge, we are the first to do that.
Remainder of this paper is constructed as follows. We first introduce somemore related work for dataset construction and action recognition in Section 2.
In Section 3 we describe our proposed approach. We then report the results ofour experiments in Section 4 and finally, conclude this work in Section 5.

2

Related WorkWe discuss here several related work on two topics: dataset construction andaction recognition.
Dataset Construction: Many recent work have tackled the problem ofbuilding qualified training datasets automatically from data retrieved by Web
search engines but most of them have been applied only on images [15, 16, 14,17]. Collins et al. [15] presented a framework for incrementally learning object categories from Web image search results. Given a set of seed images a
non-parametric latent topic model is applied to categorize collected Web image. Schoroﬀ et al. [16] proposed to first filter out the abstract images (e.g.,
drawings, cartoons) and then use text and metadata surrounding the images tore-rank the images searched in Google. Chen et al. [14] proposed NEIL (NeverEnding Image Learner) which is a program using a semi-supervised learning algorithm that jointly discovers common sense relationships and labels instancesof the given visual categories. NEIL learns multiple sub-model automatically foreach concept. As an approach which also alleviates the multi-modal problem ofconcepts, [17] divides seed images into multiple groups and trains classifiers oneach group separately. Images obtained from diﬀerent groups usually capturesome diﬀerent looks of the concept.
As for automatic construction of action datasets using unconstrained videos,
there are very few approaches as we introduced in the previous section. Moreover, these approaches require textual information associated with videos [1, 2].
Adrian et al. [3] proposed a method to learn automatically concept detectors
from YouTube videos for any kind of concepts including objects, actions andevents. Their method also requires textual description of the target concept provided by YouTube users. Furthermore, each concept must be manually assigned
a canonical YouTube category and low-quality videos are eliminated to improve
the quality of downloaded material. In this work, we propose a fully automatic
approach for action dataset building which exploits only visual features of raw
videos retrieved from video sharing sites. Our approach neither needs additional
information nor manual annotation.
Action Recognition: Most action recognition methods followed the standard framework of pattern recognition. First, a suﬃciently large corpus of training data is collected, in which the concept labels are generally obtained through4

Nga Hang Do and Keiji Yanai

expensive human annotation. Next, concept classifiers are learned from the training data. Finally, the classifiers are used to detect the presence of the actions in
the test data. We also adopt this standard framework in action recognition task,
except that instead of using provided training data, we use our automaticallycollected data to train concept classifiers.
As popular video presentation, successful hand-crafted features such as HOG,
HOF or MBH extracted along dense trajectories [18] have been adopted anddeveloped in many work recently [19, 20]. These features are generally encodedby Bag-of-Visual-Words or Fisher Vectors. In very recent years, followed by theirsuccess in image recognition field, deep learning Convolutional Neural Networks(CNNs) have received great attention and obtained promising results in action
recognition [9, 21, 22]. Following this trend, we also train a temporal CNN using
a method proposed in [9] and use this model to extract features from video shots.

3

Approach

In this work, we present an approach which autonomously extracts from noisy
Web videos relevant video shots for given action concepts. Our approach consistsof three steps: shot collection, shot clustering and shot selection. See Fig. 1 for theillustration of our proposed framework. In shot collection, we download videosof the concepts and segment them to shots. These shots are then organized
into subsets by hierarchical clustering [11]. Finally, relevant shots are ranked byoutlier factors [12] and selected from each of all clusters using a simple selection
strategy. In the followings, we explain in detail each step.
3.1

Shot Collection

We first prepare keywords for given action concepts. The concepts can be defined in any form: either “verb” (such as “dive”) or “verb+non-verb” (such as“throw+hammer”, “cut+in+kitchen”) or “non-verb” (such as “pole vault”). In
case verb included in the keyword, we search for its videos in both forms: “verb”
and “verb-ing” (such as “diving”, “throwing+hammer”). We filter out videos belonging to “entertainment”, “music”, “movies”, “film” and “games” categories
during searching since these categories generally contain extremely long videos.
Top search results are downloaded and segmented into video shots using color
histogram. RGB histograms of every frame are computed and then segmentation points are put between frames whose histogram intersection is larger thana predefined constant. Each shot represents one single scene. For each concept,
we download around 100-200 videos and obtain around 700-2000 shots.
3.2

Shot ClusteringWith shots obtained after above step, we group related shots into clusters before
shot ranking and selection. This step helps deal with concept diversity. With
web data retrieved for a given concept, there will also be common characteristics
shared among subsets of data. Therefore, rather than hard clustering data into a
specific number of subsets as some approaches which also aim to deal with intraclass variations in concepts [23, 17], we use hierarchy clustering which allows
diﬀerent clusters to share the same instances. We adopt OPTICS (“OrderingAutomatic Action Dataset Construction from Web

5

Fig. 1. Framework of our approach for automatic construction of action shot datasets
which consists of three steps: shot collection, shot clustering and shot ranking.

Points To Identify the Clustering Structure”) [11] to find clusters. Rather thanthe popular Mean Shift, OPTICS is prefer due to its computational eﬃciency.
The hierarchical structure of the clusters can be obtained based on the densityof the data distributed around their points. Follows are our brief introduction ofthis clustering algorithm. For the detail, please refer to [11].
The basic idea of a density-based clustering algorithm is that for each objectof a cluster the neighborhood of a given radius has to contain at least a given
minimum number of objects (M inP ts). Clusters are formally defined as maximal
sets of density-connected objects. We introduce here some important definitions
while briefly reviewing OPTICS algorithm.
Let p be an object from a dataset D, k be a positive integer and d be a
distance metric, then:Fig. 2. k-distance and reachability distance (k = 4)

6

Nga Hang Do and Keiji Yanai

Definition 1: k − dist(p), the k-distance of p, is defined as the distanced(p, o) between p and object o ∈ D statisfying: 1. at least k objects q ∈ D havingd(p, q) ≤ d(p, o), and 2. at most (k-1) objects q ∈ D having d(p, q) < d(p, o)Definition 2: Nk−dist(p) (p) = {q|q ∈ D, d(p, q) ≤ k − dist(p)} denotes the
k-distance neighborhood of p.
Definition 3: reach−distk (p, o) = max(k − dist(o), d(p, o)) represents reachability distance of an object p with respect to object o.
The OPTICS-algorithm computes a “walk” through the data, and calculates for each object the smallest reachability-distance with respect to an object
considered before it in the walk. A low reachability-distance indicates an object with a cluster, and a high reachability-distance indicates a noise object ora jump from one cluster to another cluster. Each cluster should hold diﬀerent
characteristics of the concept. The diﬀerences are caused by variations of conditions which videos taken under (viewpoints, scenes, illumination and so on) or
diversity in meaning of the concept itself.
3.3

Shot Selection

For each obtained cluster, we assign outlier factor for each shot based on outlying
property relative to its surrounding space. Diﬀerently from shot clustering step,in this step surrounding space of a shot is limited within in its own cluster.
We use calculation method of LOF (Local Outlier Factor) proposed in [12].
There are numerous methods of outlier detection which have been proposed so
far in the literature [24]. Among those, LOF is one of the most eﬃcient andeasy-to-implement. Especially, it makes use of computation during clustering(k − dist, Nk−dist ). Therefore, we chose it to simplify the calculation process.
Actually, the combination of OPTICS and LOF is quite natural and has been
employed in some previous work [25]. LOF of a point p is formally defined asfollows.
∑
LOFM inP ts (p) =M inP ts−dist(p)o∈NM inP ts−dist(p)(p) M inP ts−dist(o)|NM inP ts−dist(p) (p)|(1)

LOF of an object is calculated as the average ratio of its M inP ts-dist andthat of its neighbors within M inP ts-dist. A large M inP ts-dist corresponds to asparse region since the distance to the nearest M inP ts neighbors is large. In thecontrast, a small M inP ts-dist means that the density is high. In each cluster,
shots are ranked according to LOF. Shots with low LOF degrees are considered
as relevant shots and brought to the top of the cluster. M inP ts is the mostimportant parameter for finding clusters and calculating LOF. Larger M inP ts
means more clusters. Optimized value of M inP ts varies on the concept. In our
experiments, we try several values and report the one with the best performanceon average.
We propose a simple shot selection strategy which can guarantee that shots
are selected from all clusters. Let Ns be number of shots we want to collect
for a concept and Nc be number of clusters we obtained. Since some shots areshared among some clusters, simply selecting top Ns /Nc shots from each cluster
obtains less than Ns shots. Our selection strategy tries to keep selecting shotsfrom clusters which are still available until number of selected shots reaches Nsor no available clusters left. An “available cluster” must have more shots than

Automatic Action Dataset Construction from Web

7

Algorithm 1 Shot selection
Nt ← 0Nm ← Ns /Nc
for c = 1 to Nc do
C(c).is ← 1
C(c).av ← 1
end for
while Nt < Ns &∃c : C(c).av = 1 dofor c = 1 to Nc do
if C(c).av = 1 thenif C(c).ts > 2 ∗ Nm thenC(c).ns ← Nm
else
C(c).ns ← C(c).ts/2
C(c).av ← 0
end ifend ifend forfor c = 1 to Nc do
for i = C(c).is to C(c).ns do
if C(c).S[i] ∈/ S thenpush(C(c).S[i], S)
Nt ← Nt + 1
end ifend forend forfor c = 1 to Nc do
C(c).is ← C(c).is + C(c).ns
end forNm ← Nm + (Ns − Nt )/Nc
end while

twice of its maximal number of shots to be selected. This definition of available
cluster is inspired by experimental results in the baseline [6] which show that only
shots ranked among top-half should be considered as relevant shots. Selection
order for clusters is determined by the mean LOF of their shots. Our selection
strategy is summarized in Algorithm 1.
In Algorithm 1, Nt and Nm represent the total number of selected shots andthe maximal number of shots can be selected from each cluster, respectively.
C = {C(c)|c = 1 : Nc } is the group of obtained clusters. Each cluster C(c) has
following fields: C(c).is means index of start-to-select shot, C(c).ns means thenumber of shots to select from C(c), C(c).ts is the total number of shots in C(c)and C(c).av represent the availability of C(c). If C(c) is available, C(c).av = 1,otherwise C(c).av = 0. Collection of shots in C(c) is denoted as C(c).S. Since
shots are ranked as mentioned above, C(c).S[1] is supposed to be the most
relevant shot and C(c).S[C(c).ts] should be the least relevant one in cluster
C(c). S is the collection of selected shots.

8

Nga Hang Do and Keiji Yanai

4

Experiments and Results

4.1

Experimental Setup

We conduct two experiments: dataset construction and action recognition tovalidate the eﬃciency of our method. For dataset construction, we use 11 actions
defined in UCF YouTube Action (UCF11) dataset [13]: “basketball shooting”,“biking/cycling”, “diving”, “golf swinging”, “horse riding”, “soccer juggling”,“swinging”, “tennis swinging”, “trampoline jumping”, “volleyball spiking”, and“walking with a dog”. Note that in this experiment, we do not use videos of thatdataset. Our videos are automatically collected from Web source (YouTube) as
described in Section 3.2. As for action recognition experiment, we use videos ofthat dataset which contains a total of 1168 videos. The dataset is challenging
due to large variations in camera motion, object appearance and pose, object
scale, viewpoint, cluttered background and illumination conditions. We train
three SVM multi-class classifiers: one based on our collected data, one basedon data retrieved by the baseline [2] and one based on standard training data.
Finally, we use these classifiers to perform action recognition on the standard
test data.
Our baseline is our most related work [2]. According to this method, firstvideos are ranked based on usage frequencies of tags. Shots are collected fromvideos which have tags with high co-occurrence frequencies. Next shots areranked using VisualRank [10] which is a ranking method with a visual feature based similarity matrix. Since it became hard to obtain tag information,
we could not perform tag co-occurrence based video ranking step as proposed in
the baseline. Here we use our method of shot collection and apply their idea ofusing VisualRank to shot ranking to compare with our proposed method of shot
selection which composed of diversity based shot clustering and LOF based shotranking. We show that our method can obtain higher precision rate for most ofexperimented actions and importantly, our results look more diverse than thoseby the baseline in all cases.
As distance metric, we use Rank-order distance [26] which has been demonstrated as a better density measurement than commonly used Euclidean distance [17]. We train a temporal ConvNet using UCF101 dataset [13] (split 1)
following the approach proposed in [9] except that we insert a normalization
layer between pool2 layer and conv3 layer. Using this modified network architecture, we obtained slightly better performance than the original one: 82.1%
versus 81.2% [9] on UCF101 (split 1). We use 2048 dimensional full7 features
extracted using the trained temporal ConvNet. M inP ts is set as T /n where T is
total number of shots for a concept obtained after shot collection step (Section
3.1) and n is a constant. Ten values of n are experimented: 10, 20,..., 100.
4.2

Dataset construction

In this experiment, we want to validate the quality of automatically constructed
dataset regarding precision and diversity. Precision rate is calculated as percentage of relevant shots among top N shots following our baseline [2]. Three valuesof N are taken into consideration: 30, 50, 100. We evaluate relevance of upper
ranked shots manually.
First we examine the eﬀect of parameter settings on the performance of our
proposed approach. Figure 3 shows average precision rates in diﬀerent cases ofAutomatic Action Dataset Construction from Web

Fig. 3. Average precisions by the proposed method in diﬀerent cases of n when
N = 100.

9

Fig. 4. Average precisions by the proposed approach and the baseline in different cases of N .

Table 1. Precision rates of 11 action keywords with N = 100.
Action
Proposed Baselinebasketball50
35
biking23
17
diving35
28
golf swing
52
54
horse riding
50
42soccer juggling
68
63
swing
36
31
tennis swing
47
51
trampoline jumping
54
54
volleyball spiking
58
69
walking
14
9
Average
44.3
41.1

n with N = 100. According to our empirical results, n = 50 obtained best
performance. All results related to our proposed method that we report fromhere on refer to the case of n = 50. Figure 4 compares average precision rates in
diﬀerent cases of N between the proposed approach and the baseline. As shownin this figure, the proposed approach outperformed the baseline in all cases of N ,especially when N ≥ 50. In all of our results, “Proposed” means our approachand “Baseline” corresponds to VisualRank based shot ranking with our shotcollection. Precision for all actions when N = 100 are shown in Table 1.
As shown in Table 1, for most of the actions, more relevant shots could be
ranked to the top using our method. In many cases, top ranked shots by thebaseline are although relevant to the concept but actually look similar to eachother (See Figure 7 for some example results). Even though average precision is
not significantly improved, shots retrieved by our proposed method look much
more diverse as shown in the followings.
Regarding evaluation for diversity of ranking results, we use evaluation method
as described in [7]. Diversity score of a ranking result is defined as the ratio of
the number of identical videos in its top ranked N video shots to N . This definition is based on the fact that shots from the same video tend to look similar.
The results of diversity evaluation are summarized in Figure 5. As shown in
Figure 5, overall, the diversity score was significantly enhanced by using the

10

Nga Hang Do and Keiji Yanai

Fig. 5. Results of diversity evaluation. As
opposed to the baseline, our approach
retrieved more diverse shots from various videos. This explains for significant
improvements in recognition performance(Figure 6).

Fig. 6. Results of action recognition with
automatically obtained training data. As
shown in this figure, action model trainedwith shots obtained by the proposed
method achieved better recognition rates
in all cases of N .

proposed method. Figure 7 shows some examples of relevant shots among top
15 shots obtained by our method and the baseline.
4.3

Action Recognition

In this experiment, we validate performance of our automatically collected training data for the task of recognition on standard test data. To evaluate recognition
performance, we follow the original setup [13] using leave one out cross validation
for a pre-defined set of 25 folds. Average accuracy over all classes is reported as
performance measure. We use top ranked N shots to train action classifiers. Similar to the previous experiment, three values of N are taken into consideration:
30, 50 and 100.
Figure 6 shows recognition accuracy rates by the proposed approach andthe baseline in all cases of N . As shown in this figure, we obtained significant
precision gain compared to the baseline. The recognition precision was boosted
from approximately 35% to 44% in case of N = 100. This can be explained
mostly by the improvements regarding diversity in the results (Section 4.2).
Since many shots obtained by the baseline look similar, the information we cangain from them is much less than that from shots retrieved by our proposed
approach. These results verified the fact that a high quality action databaseshould reflect as well as possible the diversity of the concepts. The precision rate
is further improved as more top-ranked shots are used to train.
In case of using standard training data, the average recognition rate was
81.5%. This result is comparable to other approaches on the same dataset [27, 18].
[27] with probabilistic fusion of multiple motion descriptors and scene context
descriptors achieved 73.2%. Especially, the state-of-the-art motion hand-craftedfeatures (dense trajectory based MBH) [18] achieved 80.6%. To the best of ourknowledge, there are no reports with CNN features on this dataset for us tocompare.

5

Conclusions

In this paper, we proposed a fully automatic approach for action dataset construction with noisy Web videos. Our approach aims to solve the problem of lim-

Automatic Action Dataset Construction from Web

11

Fig. 7. Relevant shots among top 15 shots retrieved by our proposed method and thebaseline for “golf swing” and “horse riding”. As seen here, shots by the baseline tend
to look similar while shots by our method are taken from various view points against
diﬀerent background.

itation in quantity of training data for the task of action recognition. In our experiments, we first constructed a database for 11 actions in UCF11 dataset using
YouTube videos with our proposed approach. We then employed this databaseto train action classifiers and applied them to classify standard test data of
UCF11. Even though collected training data by it is still far from “clean data”
as standard training data, it oﬀers the advantage of a fully automatic, scalable
learning and shows the potential for development of approaches for automaticconstruction of action databases.

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