Journal of Robotics, Networking and Artificial Life, Vol. 2, No. 4 (March 2016), 234-237

Fast collective photographic subject detection without pixels by an assumption about a shootand its elevation angle
Sora Tanioka

Department of Computer Science, National Defense Academy of Japan, hasirimizu 1-10-20
yokosuka, kanagawa 239-8686, Japan
Masao Kubo

Department of Computer Science, National Defense Academy of Japan, hasirimizu 1-10-20
yokosuka, kanagawa 239-8686, Japan
Hiroshi Sato

Department of Computer Science, National Defense Academy of Japan, hasirimizu 1-10-20
yokosuka, kanagawa 239-8686, Japan
E-mail: {masaok,hsato}@nda.ac.jp
www.nda.ac.jp/cs/

Abstract
In this paper a method that discovers socially attracted photographic subjects in real time is proposed. This needs
only non-pixel information of a digital photograph for this discovery which include a time of shoot, GPS location
where a photograph is taken, a bearing of the shoot and so on. Thinformation is sufficiently lightweight so that we
expect that a congestion-free photo sharing wireless network service can be achieved. In this paper, a photography
behavior model which considers an elevation angle of a shoot makes the accuracy better.
Keywords: Reality mining; Photograph, Event detection, Geo tagged images

1. Introduction
Interests at the present moment of others can beobserved by current Information Communication
Technology. Photograph based communication is more
vivid and secure than a text based information sharing
like Twitter. However, this communication system
usually becomes much slow because the heavy
congestion caused by transmission of hug pixel data.
Therefore this framework has not been used for a realtime information sharing system. So far researches
which attempt to improve this lower real time capability
have been proposed. It has been proposed a method oftransmitting only the best photos that match the viewing

request to the server while they are categorized only by
not-pixel information of digital photograph. The
information includes time to shoot, GPS and bearing ofshoot so on. According to this method, photo pixel data
congestion is unlikely because they are transmitted
sequentially. However, in the conventional method,
there is a practical problem that estimation accuracy ofthe object taken from the biased direction is worse. In
order to solve this issue in this paper, we propose amethod for estimating sterically an object by using theelevation angle (Fig.1).It is intended to improve theestimation accuracy by noting the characteristic of

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Sora Tanioka, Masao Kubo, Hiroshi Satotilting the camera in order to capture the entire subject
into photographic frame.
This paper is organized as follows. First, related studies
are introduced. Next the algorithm by using theelevation angle information is proposed. Finally,
computer simulations perform, we verify the validity of
the algorithm.

Fig.2 (a)A case conventional methods solve. (b) A difficult
case for the conventional methods.

Fig.1 The concept of the poposed method: estimation ofsubject by a colletion of elevation angle data in non pixel
information in digital photographs.

2. Related works
Kubo et al. [1] introduces the photographer behavior
model using the angle of view by composition. Thereby
it realizes a method of automatically detecting its main
subject in each image. As shown in Fig.2(a) the methodthat adopts a planar estimation in estimating the subject
can be very accurately estimated when it is taken with awide angle to the object. However, such a examples arerare in the real environment. Also when it is taken froma narrow angle, it can be pointed out that the accuracy ishardly improved even if information is increased asshown in Fig.2 (b).

Fig.3 Subject location discovery algorithm by MAP estimation

3. The proposed system

3.2. Flow of the proposed system

3.1. Input data

Fig.3 describes the proposed algorithm. In this figure aset of photographic information is described byC�= {c1,L ,c N } . The each subject is estimated by MAP
�(Maximum a posteriori) estimation iteratively. Once asubject is found, ci which seems to be a consequence ofshooting the subject is removed from C. By thisprocedure, the proposed method can discover multiplesubjects.

Here, small mobile devices such as a smart phone is ourmain subject. The photographic information includes
latitude, longitude, altitude, azimuth of GPS, elevation
angle by accelerometer, and time to shoot in TIFF andExif data .

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Fast collective photographic subject

3.5. Photographer behavior and elevation angle
Fig.4 illustrates the top 20 of the search result of“building” by Google image search. As these images
tell, the elevation angle is nearly horizontally when aphoto is taken from far. On the other hand, when it istaken from the vicinity of a building the elevation angle
becomes large in order to include the top of the buildingin the photo. Thus it was found to be estimated by theelevation angle at the time of photographing thedistance from the size and shooting point of the object.

Fig.4 Search result of ‘building+photograph’ by Google Image
Search

3.3. Photographer behavior model
The photographer behavior model is a probability
distribution Pshoot(cTi|sj) （ that a photographer at cTi
takes a photo for a given subject s j .
(1)Pshoot (cTi s j ) = K motivation ⋅Vcomposition (s j ).
where Kmotivation represents the motivation to take aphoto and Vcomposition(sj)means the fitness of thecomposition. This consists of the elevation angle E andthe bearing B.
(2)
Vcomposition (s j ) = f (E) f (B)However, in this paper, we assume that the photographsthat place a subject in the center of the frame are useful
to viewers and f(B)=1.
3.4. Algorithm for estimation of subjects
First, the overview of the MAP estimation is described.
|c j |

log ϖ j,k =∑ max{log Pshoot (cTx o j,k , ∆ x ),l 0 } + l 0x =1(3)

For each grid s j in a given area, a weight as Pshoot(cTi|sj)
is added about C. After this weighting step, a grid ofmaximum weight is selected as a subject s in thisiteration. ci which seems to take a photo of this subject
is removed and s with ci is added the subject set S. The
next iteration starts with the new C. If C is empty thealgorithm stops..

Fig.5 Examples of the proposed photographer behavior modelof elevation angle

3.6. The proposed photographer behavior modelof elevation
The photographer behavior model of elevation f (E) is
proposed in this section. This f (E) is used for the Mapestimation procedure in the algorithm in 3.4.
(4)
(E − D) 21)
exp(−Vcomposition (s j ) = f (E) =2s2psH(5)D = arctan( )2LThe composition of a photograph is evaluated by anelevation angle E. We suppose that distribution of E
obeys a normal distribution. D is the best elevation
angle and H is the height of the subject and L is thedistance to the subject.σis a parameter.
Fig.5 shows examples of this f (E) .The motivation
Kmotivation is constant among all photographer. The y axis
indicates E(rad). The x axis shows the evaluationVcomposition. There are 3 lines, namely the thin line, thedotted line, bold line which represent the evaluation ineq.(4) for a tall building of the height of 50 m. The thinline shows a case when a photographer takes it from far
and it can takes a good photograph but it seems to be

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Sora Tanioka, Masao Kubo, Hiroshi Sato

difficult because the suitable elevation angle is less thanthe 2 remaining cases. This trend seems to be consistentwith our experience.
4.

Experiment

By performing an experiment by computer simulations
to evaluate the performance of the proposed method.
The experiment is conducted in the configuration of
estimating the subject that can be taken only from thedirection of the difficult had been part in theconventional technique. Only one subject with theheight of 40 meters sets at （ x,y） ＝ （0,0） . The
dataset of photographic information is generated byMetropolis-Hastings algorithm. For reduction ofmemory resources, each elevation angle is discretized
into 18 pattern from 0 degree to 85 degree.
Fig.6 shows the result of estimation of location of thesubject. The bullet at (x,y)=(0,0) indicates the subject.
The result by the traditional method is (x,y)=(4,-46)
represented by ‘+’ mark. On the other hand, the result of
the proposed method is (x,y)=(-6,8) shown by a ‘x’mark. The proposed method can improve the accuracy.
The Fig.7 shows the result when there are 2 subjects in agiven field and the permitted area to take a photo isrestricted also. In this case, our method can estimate thesubjects’ location simultaneously.

Fig.6 the result of the experiment

5. Conclusion
Collect only the shooting information of digital photos,
we have proposed a method for estimating a subject thathas attracted the social attention. The proposed a
photographer model using elevation and improved
conventional been difficult case. Consequently, it waspossible to improve the practicability.
This work is partially supported by JSPS 15K00433.
References
1. Y. Kubo, M. Kubo, H. Sato, M. Hirano, and A.
Namatame, Understanding Geographic Attentions ofCrowd from Photographing Information in Journal ofAdvanced Computational Intelligence and Intelligent
Informatics, Vol. 17, No. 6(2013) 890-903.

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Fig.7 the result of the experiment