A Personal Photograph Browser for Life Log Analysis
based on Location, Time, and Person
Ai GOMI

Takayuki ITOH

Ochanomizu University
2-1-1 Otsuka, Bunkyo-ku
Tokyo 112-8610 Japan

Ochanomizu University
2-1-1 Otsuka, Bunkyo-ku
Tokyo 112-8610 Japan

gomiai@itolab.is.ocha.ac.jp

itot@is.ocha.ac.jp

ABSTRACT
Image browsers are important and useful applications for retrieving images from personal photograph collections. Suchbrowsers can be a life log analysis tool to explore the eventsof photograph owners. This paper presents a novel photograph browser consisting of two linked views. One of theviews displays photographs clustered based on their locations and times, and the other displays people clusteredbased on their co-occurrences in the events. Specifying aphotograph, the corresponding time the picture was shotand people in the photograph are highlighted. Specifying
a time, corresponding photographs and people are highlighted. Specifying a person, associated photographs arehighlighted, and their corresponding times are shown. The
mechanism helps users to discover interested photographs
and understand the events of photograph owners. This paper presents a real scenario and user experiment, demonstrating the eectiveness of the presented browser.

Categories and Subject Descriptors
H.5.2 [Information Interfaces and Presentation]: User InterfacesGraphical User Interfaces (GUI); I.4.9 [Image Processing and Computer Vision]: Applications

General Terms
Algorithm, Design, Experimentation.

1. INTRODUCTION
The recent revolution of digital camera technology has resulted in much larger photograph collections. Many camera
users store tens of thousands of photographs, and such photographs are often expected to be used as life logs. Imagebrowsers are important and useful applications to view andinspect such photograph collections. We also expect thatthey are useful for the analysis of personal life logs. We expect that by creating a novel photograph browser we canquickly understand the timeline of the events of photograph

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owners, visited locations, their social networks, their favorite
scenes to shoot as photographs, and various interesting information.
Generally, we need attributes associated with the photographs, so that we can smartly organize, browse, and retrieve them. We created a questionnaire asking "Which attributes do you think is important to retrieve specic photographs from your personal collections?" We obtained a
result that time, location, keywords, features (e.g. color),and people (e.g. owner, photographer, people taken in thephotographs) are important attributes for photograph retrieval [3]. Here, we do not think keywords and features arealways reliable for photograph retrieval, though there havebeen many research works on content-based image retrieval(CBIR) techniques which deal with keywords and features.
Reecting the questionnaire results and the above discussion, our study focuses on using time and location information for browsing and the analysis of personal photographcollections. We developed an image browser MIAOW [3],which displays photographs hierarchically clustered basedon their locations and times. MIAOW utilizes a view of a
3D space with an orthogonal coordinate system to place a
set of photographs; it assigns two axes (X and Y in this paper) to the shooting locations of the photographs, and theother axis (Z axis in this paper) to their shooting time.
In addition, we think a person's identity is also good information to retrieve personal photographs. We often look
for photographs of specic people with location and/or time
conditions, such as "I want all photographs taken of mychildren during travels", or "I want all photographs takenof my specic classmate during 2006 to 2008". It is notalways easy to look for such sets of photographs, if the owners of the photographs simply store them by dividing into
location- or time-based folders, or using existing commercial
photograph management software or Web services. On theother hand, recent novel face recognition techniques have realized almost perfect recognition of people from photographcollections, and some of the techniques have been publishedas Web APIs. We therefore think it is valuable to develop
personal photograph browsers that use personal identies
for interactive photograph retrieval.
This paper presents an extension of a personal photographbrowser MIAOW [3], which displays photographs hierarchically clustered based on their locations and times, as wellas people clustered based on their co-occurrences. The extended browser displays a collection of photographs in a viewof a 3D space, as previous MIAOW displays. This paper
calls this view LTView (Location and Time View). In addi-

LTView
XYplaneRotation

XZplaneZoom in

Zoom inPView

XZ-planephotograph in PView. If time is selected, it highlights thecorresponding photograph in LTView, and people in thephotograph in PView. If a person is selected in PView, it
highlights photographs which contain that person, and thetimes of the highlighted photographs in LTView. We expect that the browser makes it easier for users to search for
specic photographs, associated with memories of locations,times, or people.
This paper introduces a detailed algorithm used in thepresented browser, a use case scenario using a real personal photograph collection, and the results from user experiments.

2.

PHOTOGRAPH BROWSER BASED ON
LOCATION, TIME, AND PERSON

2.1

Technical Overview

The proposed technique supposes that an unorganized setof photographs is given as an input dataset. We assume thatlocation (longitude and latitude) and time are assigned tothe photographs. Also, we assume that people taken in eachof the photographs are specied by existing face recognition
services. As a result, we describe the i-th photograph, pi ,as follows:
pi = (φi , λi , ti , f1i , f2i , fmi i )Figure 1: The presented photograph browser. LTView displays all photographs clustered based on their locations andtimes in a 3D space. LTView assigns location to X- andY-axes, and time to Z-axis, and places all the clusters of
the photographs. LTView rst displays representative photographs of the clusters, and then displays all photographs
in the clusters while zooming into the clusters. PView displays all faces of the people appeared in the photographs.
The people are clustered based on their co-occurrences in
the photographs.

tion, the browser provides another view to display people in
the photographs. This paper calls this view PView (Person
View). Figure 1 shows a snapshot of the presented browser.
As a preprocessing step, the technique rst applies a clustering algorithm to divide the photographs based on theirlongitudes and latitudes. It then divides the photographs
contained in each cluster based on their times. Consequently,
a collection of photographs constructs a two-level hierarchical structure. At the same time, the technique extracts people from each of the photographs by applying a face recognition method. It then constructs a photograph-person matrix, and applies a clustering algorithm to the people, toconstruct groups of people according to their co-occurrences.
The presented browser places all clusters onto XY-, XZ-,
and YZ-planes of a 3D space in LTView, representing the hierarchy of photographs as nested rectangular regions. Also,
the browser places all person clusters as nested rectangular regions in PView, which has a linkage mechanism with
LTView. If a user selects a photograph, it highlights thecorresponding time in LTView, and people contained in the(1)Here, φi is the latitude of pi , λi is the longitude of pi , ti isthe time of pi , fji is the j -th person taken in pi , and mi is
the number of people taken in pi .
Also, we suppose functions to calculate the 3D position(xi , yi , zi ) of pi in LTView as follows:
xi = f (λi ), yi = g(φi ), z = h(ti )(2)

Our current implementation simply applies linear functions
to f , g , and h. It assumes that longitude lines are vertical,
and latitude lines are horizontal with respect to the display
space. Consequently, our implementation places clusters ofphotographs on a Mercator projection map. We decided toapply a Mercator projection because it is one of the mostpopular projection; however, we can apply other various projections as well by replacing the functions f and g .
As preprocessing, our technique rst divides photographs
according to their x and y values, and constructs higher-levelclusters of photographs. It then divides the photographs ineach of the clusters according to their z values, and constructs lower-level clusters of photographs. This paper calls
the lower-level clusters events. Consequently, it constructs a
two-level hierarchy of the photographs. After the clustering
process, it selects a representative photograph for each cluster. In the current implementation, each representative photograph is selected randomly . Meanwhile, the technique divides people according to their co-occurrences in the events.
The technique then places the photographs into LTView,
and people into PView. It places the photograph clusters as
nested rectangular regions onto XY-, XZ-, and YZ-planesin LTView. Also, it places the person clusters as nested
rectangular regions onto a screen in PView. Here, we use a
3D space and an orthogonal coordinate system in LTView,
where X- and Y-axis correspond to location, and the Z-axis
corresponds to time. The technique rst calculates the positions of clusters on the XY-plane. It represents the two-level
hierarchy as nested rectangles, while avoiding overlappingthe rectangular regions at the same level and attempting tominimize the display space. The technique nally calculates
the positions of clusters on the XZ- and YZ-planes.
The technique is an extension of MIAOW [3], a previous version of our photograph browser. Existing features of
MIAOW and new contributions in this paper are as follows:…
Phase 1-1: Location-based photo clustering
Location 1

Location 2tExisting features of MIAOW: Photograph clustering (see Steps
1-1 and 1-2 in Section 2.2), and placement in LTView(see Section 2.3).

tPhase 1-2: Time-based photo division to “events”

New contributions in this paper: Person clustering (see Steps
1-3 in Section 2.2), placement in PView (see Section2.4), and interaction between LTView and PView (seeSection 2.5).

Event 1-1ab

Event 1-2

Event 2-1

a

a

c

d

e

b

Event 2-2a

2.2 Photograph and Person Clustering
For preprocessing, the technique constructs clusters ofphotographs and people. The clustering process consistsof the following three phases, as shown in Figure 2, where
Phases 1-1 and 1-2 have been presented in [3]:Phase 1-3: Co-occurrence-based person clustering
Event

b2 e
Group
P
e
rs
o
n

Phase 1-1: Constructs high-level clusters of photographs basedon their locations.
Phase 1-2: Divides photographs in each of the high-levelclusters based on their times, and constructs low-levelclusters (called "events" in this paper).
Phase 1-3: Constructs clusters of people based on their cooccurrence in each of the events.
Image clustering has been an active research topic. Naaman et al. presented an image clustering technique [13] that
rst divides images based on their times, and then divides
again based on their locations. On the other hand, it isimportant to divide photographs by locations rst in our
technique, because our design aims to place all clusters ofphotographs based on their locations on the XY-plane.
Phase 1-1 simply calculates distances between any arbitrary two photographs on the XY-plane, then constructs a
dendrogram based on the distances, and nally divides thephotographs according to a predened distance threshold.
After the clustering, it calculates the average longitude andlatitude values for each cluster, and use them for locationbased rectangle placement as described in Section 3.3. We
empirically determined the threshold of clustering as 0.01,
which corresponds to (0.56234 degree) / (111 km) on theequatorial line. This phase may generate extremely large
clusters, and in this case it is desirable to divide the photographs again using smaller thresholds. Also, this phase
may put photographs near the borders of multiple clusters
into undesirable clusters. In this case we may need to taketimes into account to adequately select the clusters of photographs near the borders.
Phase 1-2 simply sorts the photographs in each of thehigher-level clusters based on their shooting times, then constructs a dendrogram based on the time dierences, and nally divides the photographs according to a predened difference threshold. After the clustering, it nally calculates
the average time values for each cluster, which are used fortime-based rectangle placement as described in Section3.3.
Through empirical analysis, we determined the threshold ofclustering as 3.374 days. However, we may need to adaptively control the threshold so that we can generate semantically adequate clusters.

Group 1

c 3 d
Group
a

b

e

c

d

a

Figure 2: Photograph and person clustering.
Phase 1-3 rst counts the number of photographs qij clustered in the i-th event, containing the j -th person. Here thej -th person can be represented as a n-dimensional vector(q1j , q2j , ..qnj ), where n is the number of events. It thenconstructs a dendrogram of the people according to the Euclidian distances between the n-dimensional vectors of arbitrary pairs of the people. Finally, it divides the people bycutting the dendrogram according to a pre-dened thresholdvalue.
Our implementation uses Google Picasa API for face recognition. The implementation rst sends all photographs to
the API. The API clips facial images from the photographs,
and returns groups of clipped faces. Here, each face groupcontains faces which are determined as the same person.
The implementation assigns IDs to the groups, and picks up
a representative face image for each of the sets. Our implementation does not assign labels (e.g. person names) to theface groups, but just shows representative face images of thegroups on PView.

2.3

Photograph Placement in LTView

Photograph placement in LTView has been presented in[3]. LTView applies a Treemap-like space-lling hierarchical data visualization technique [9] to place the photographclusters by the following phases:
Phase 2: Places a set of clusters as nested rectangles onto
the XY-plane, reecting the latitudes and longitudesof the clusters.
Phase 3a: Places a set of clusters as nested rectangles onto
the XZ-plane, reecting the times of the clusters, andX-coordinate values calculated in Phase 2.
Phase 3b: Places a set of clusters as nested rectangles onto
the YZ-plane, reecting the times of the clusters, andY-coordinate values calculated in Phase 2.

2.4 Person Placement in PView
The technique applies a Treemap-like space-lling hierarchical data visualization technique [9] to place the persongroups onto a screen in PView. It places a set of photographsof people onto a display space consisting of the following twophases:Phase 4-1: Places a set of photograph thumbnails of thepeople using grid layout, and encloses them by a rectangular border. It repeats this process for all of thegroups.
Phase 4-2: Packs all the rectangles corresponding to the person groups into a rectangular region.
The person groups are often meaningful (e.g. family, classmates, colleagues), and even more so if we can manually assign names to the groups. Our implementation can display
such group names along the rectangular borders in PView.

2.5 Linkage between LTView and PView
We implemented an association mechanism between LTView
and PView, as shown in gure 3.
Draw segmentsfrom photograph tocorresponding positions

World Map

Specifying a photograph:- Highlight corresponding time
- Highlight persons shot in it
LTViewy3.

xSpecifying a time:-Highlight photographs- Highlight persons

PView
Specifying a person:- Highlight times when
he/she was shot
- Highlight places where
he/she was shot

Figure 3: Association between LTView and PView.
Our implementation of LTView places a world map behind the XY-plane. Also, it places a set of colored small
rectangular elements, called "timeline elements" in this paper, which correspond to events and are sorted in the orderof their times along the Z-axis. Here, all photographs andtimeline elements in LTView are cursor-sensitive. Also, allpeople in PView are cursor-sensitive.
If a user picks a photograph by the cursor, our implementation takes the following actions: 1) it highlights the

EVALUATION

We implemented the technique with Java JDK 1.6.0, andtested on an IBM ThinkPad T500 (CPU 2.8GHz, RAM
2GB) running Windows XP SP3. We used our personal collection consisting of 4,804 photographs of size 100x75 pixels
stored in JPEG format as thumbnails or representative photographs. The technique constructed 38 high-level clusters
and 73 low-level clusters (=events) from the photographs,
and recognized 52 people.

3.1

Timelineelementsztimeline element corresponding to the event to which thephotograph belongs to, 2) it highlights the people containedin the photograph in PView, and 3) it draws a connection
line from the photograph to the corresponding position on
the world map.
If a user selects a timeline element by the cursor, ourimplementation takes the following actions: 1) it highlights
a set of photographs belonging to the event corresponding
to the specied timeline element, 2) it highlights the people
contained in the photographs in the event in PView, and
3) it draws connections from the set of photographs to thecorresponding positions on the world map.
If a user selects a person in PView by the cursor, ourimplementation takes the following actions: 1) it highlights
a set of photographs including the person, 2) it highlights
the timeline elements if their corresponding events containphotographs with the person, and 3) it draws connections
from the set of photographs to the corresponding positions
on the world map.
Our implementation draws borders of photographs or people in vivid colors as they are highlighted. Also, it draws
timeline elements in vivid colors as they are highlighted.

ExamplesThis section shows several examples of visualization results using three datasets. Figure 4 (Upper) shows an example of displaying the XY-plane. Our implementation displays a world map behind the XY-plane, and draws connecting lines between higher-level clusters and corresponding positions on the map. These results demonstrate that the technique places representative photographs of clusters without
overlapping the photographs, attempts to minimize the display area, and preserves the geospatial adjacency among theclusters. Figure 4 (Lower) shows an example of displaying
the XZ-plane.
An user evaluation of LTView is introduced in [3]. We
asked them to search for specic images specied by various
conditions, and measured the time it took them to locate
the photographs. We compared LTView and an existing 2D
image browser [4], and demonstrated that subjects took 3.5
times longer in average when using the existing technique,and 4 times longer in maximum.

3.2

Real-World Scenario

We visualized a set of photographs of a female student
taken from 2004 to 2009, and reviewed her story.
Figures 5 and 6 show visualization results by clicking eachyear of the timeline elements during 2004 to 2009. Figure 5(Upper) shows that she mainly took photographs with herclassmates in Europe and a Pacic island in 2004. Figure
5 (Center) shows that she took many pictures with laboratory classmates and her family in 2005 in various locations
throughout Japan. Actually, that year was her rst year

China,Korea

EuropeJapan

U.S.A.
(Western&Rocky)

U.S.A.
(Eastern&Southern) Caribbean sea
Asia

Australia

E. We expect that this browser may bring opportunities torevive old friendships as shown in this example. Figure 8
shows a visualization result of exploring photographs of hersister. The result shows that the latest event with the sister
was in 2007. At rst the photograph owner did not understand why she did not have events with her sister since2007, and what the latest recorded event was. Zooming into the highlighted representative photograph and looking at
the photographs in the highlighted event, she found thatthe event was her sister's wedding party. She realized that
it was not easy to plan events with the married sister living
far away, and thought of planning future events and inviting
her sister. This zooming operation allows users to navigate
event details by displaying all photographs of the events,and helps them to remember the details.

3.3

User Experience

We asked 12 subjects to experiment with the presented
browser and the provided dataset. We briey explained
them how to operate the browser and what kind of photographs the dataset contains in several minutes, and gave
10 minutes to the subjects to freely experiment with them.
Then, we asked them to describe the characteristics of theevents they found while experimenting with the browser.
Below are the common characteristics the subjects determined:

Figure 4: LTView. (Upper) XY-plane. (Lower) XZ-plane.

in her laboratory, and therefore she took many photographs
since every event was novel and full of new experiences. Figure 5 (Lower) shows that she shared events with classmatesagain, and also with some of her family members in 2006.
She went to various cities in Europe, and a Caribbean island
for the rst time. Figure 6 (Upper) shows that she went tomany places in the USA in 2007, although she did not go to
the USA in other years. On the other hand, she did not go
to Europe during that year, although she went there in allother years. That year she attended fewer events with herclassmates. Figure 6 (Center) shows that she attended many
events with her new colleagues. Figure 6 (Lower) shows that
she had a fewer events abroad. The result demonstrates
that people and places in her events dynamically vary every year. Users can easily understand which year the photograph owner had many events at specic places or withspecic people.
Figure 7 shows a visualization result by selecting people
in a specic group. Figure 7 (Upper) shows that she did
not have events with friend E after 2006. Figure 7 (Lower)shows that she had events with friend F three times, in 2008
and 2009. Comparing the two results, we understand thatthe three events in 2008 and 2009 were in her home country, and it seems that she could have invited friend E to theevents as well. Discovering this fact, the photograph owner
thought that she should plan the next event inviting friend• She went to the USA only in 2007. She went to Europe
in other years.
• She had many events with her colleagues since 2008.
• Her father joined every event in her hometown.
• She went to foreign countries with specic people. Other
people joined the events only in her home country.
• She had events with classmates frequently.
• She had events with her family every year.
• A baby became part of the events since 2008.

We found that each examinee quickly determined correct characteristics. This result demonstrates that the presented browser is useful for life log analysis of specic people
with their photographs. They felt that the browser is user
friendly, and intuitively understood the characteristics bylooking at the photographs with location, time, and person information. Especially they felt informed by the factthat people were clustered, because they could explore photographs group-by-group.
At the same time, we asked them to give us comments andsuggestions on how to further improve the browser. Below
are the feedback items from the subjects:• It is informative if the number of participated eventsof a person is represented using colors.
• It is useful if the world map is clickable and events canbe selected on the map.
• It is informative if countries or regions of clusters are
represented by colors.
• It is useful if timeline units can be selected by season
or month, not only by year or event.
• The view is clearer if unselected events are hidden.

Especially, rst two items are suggested by multiple subjects. These suggestions can all be implemented by small
extensions to our current implementation. We plan to implement them and re-evaluate the browser's usability.

4. RELATED WORK
4.1 Image Browsing
Many image browsing interfaces, such as image search engine Web sites, simply provide a set of images in grid layout in the ranking order. These kinds of interfaces are notalways eective for quickly nding all the desired images.
More sophisticated user interfaces for image browsing canbe valuable to more easily explore personal image collections.
Some browsing techniques focus on the layout of image
thumbnails so that they can nely represent content similarity among the images. We can embed images in a lowdimensional Euclidean space preserving the distances between pairs of them, using, for instance, multidimensional
scaling (MDS) [15]. These techniques are good at representing distances among images; however, it often overlaps
many displayed images. We would like to argue that users
may prefer a grid-like layout for the display of image collections, since with this, layout images never overlap. This
problem can be solved by applying techniques for grid-basedlayout of multi-dimensional datasets [2].
Structures such as trees or graphs are also eective forphotograph browsing. Bederson presented PhotoMesa [1],which places groups of images into rectangular subregionsof display space. Kustanowith et al. presented a technique[12] that radially places clusters of images, and provides focus+context representation. We presented an image browser
CAT [4], that represents clusters of images as nested rectangular regions. We also presented MIAOW, which displays
location- and time-based clusters of photographs as nested
rectangular regions [3].

4.2 Hierarchical Data Visualization
There are many well-known works on hierarchical datavisualization, where a large portion of them are categorized
as tree-based approaches, and the others are space-lling
approaches. Since the goal of this paper is the all-in-one visualization of clustered images, space-lling approaches areappealing for browsing of large collections of them.
LTView applies a Treemap-like space-lling hierarchicaldata visualization method based on nested rectangular packing [9]. It places thousands of leaf-nodes into one display
space while satisfying the following conditions: 1) no overlapping between the leaf-nodes and branch-nodes in a single hierarchy of other nodes, 2) drawing of all leaf-nodes
by equally shaped and sized icons, 3) minimization of thedisplay area, and 4) minimization of aspect ratio and thearea used for rectangular subspaces. A desirable trait of thetechnique is the representation of lower-level data items as
clickable and equally-sized thumbnails. Moreover, the technique has the capability to control positions of rectangles byreferring to templates, which describe the ideal node positions, as described in Section 5 of [9].

4.3

Geography-based Visualization

LTView places rectangular regions onto a 2D space while
attempting to reect their pre-assigned positions. There
have been many visualization techniques that represent data
items as rectangles and place them reecting pre-assigned
positions. The hierarchical data visualization technique applied to the image browser [9] is a typical technique that
attempts to reect the pre-dened positions.

Heilmann et al. presented RecMap [7] that represents geographic items (e.g. states in the USA) as rectangles while
preserving important geo-spatial constraints. It attempts
to minimize the errors of areas, shapes, topology, and positions of the rectangles, and empty area, while it preserves
other constraints of the rectangles, by applying optimization schemes. Wood et al. presented Spatially Ordered
Treemaps [16] that calculates orders of nodes based on spatial consistency and divides a rectangular display space by
the calculated order. It can generate good cartograms while
it inherits good properties of Treemaps.
LTView places collections of photographs in a 3D space,
where three axes of the 3D space correspond to longitude,
latitude, and time. Such space has been well studied asa space-time cube, since time and location are quite relevant together while observing human behavior [5]. Several
visualization works in space-time cube model [11] demonstrated the usefulness for observation of human life. Suchspace-time information has been recently applied to image
browsing techniques [14].

4.4 Friendship Visualization
Friendship is interesting information to be visualized. We
can obtain large-scale friendship network data, due to recent
drastic evolution of social networking mechanisms. Some of
the works implemented the visualization of people clusters
analytically or interactively [6] [8]. Our technique also visualizes people clusters; however it constructs the clusters fromtheir co-occurrences in personal photograph collections.

5.

CONCLUSION AND FUTURE WORKThis paper has presented a personal photograph browser
based on locations, times, and people. The technique rstapplies a clustering algorithm to a set of photographs basedon their locations and times, and treats the clusters as events.
It then clusters people based on their co-occurrences within
the events. The browser provides two views: An LTView
that places the photographs into a 3D space based on theirlocations and times, and a PView that visualizes clusters ofpeople. We implemented a linkage mechanism between thetwo views, so that users can conveniently explore the photographs. The paper presents a real-world usage scenario
of exploring a personal photograph collection, and a user
experimental result.
Our current implementation of pre-processing is almost
automated, except the selection of representative photographs.
Some subjects also suggested preferable representative image selection criteria; such as shooting time, frequency ofaccesses to full-size images, and sizes or numbers of faces
taken in the images. Also, image ranking [10] may be agood factor to select representative photographs. We will
test such criteria.
In addition, we will perform experiments with larger photograph collections, and additional user evaluations.

Acknowledgements
We appreciate Dr. Kang Zhang of the University of Texas
at Dallas, for helping us improve this paper.

6.

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2004
Classmates

2005

Lab

Family2006

Classmates

Figure 5: Scenario(1). Selecting each year from the timeline
elements, during 2004 to 2006.

PView
Select friend E
2006

2007

No events since 2006

LTView

FamilyPView
Select friend F
2006

Colleagues

2008

2008

2009

LTViewThree events in 2008 and 2009

Figure 7: Scenario(2). Events with classmates.

2007
Select the sister
Final event withher was in 20072009

Zoom up to the highlighted photograph

Figure 6: Scenario(1). Selecting each year from the timeline
elements, during 2007 to 2009.

Figure 8: Scenario(3). Final event of a specic person.