1 I ntroduction

T he rapidly increasing use of digital cameras is caus-ing a corresponding increase in the number and sizeof personal digital photo collections. T hese collec-tions routinely contain thousands of photos andrequire effective interfaces that facilitate browsing,manipulation, and sharing. Studies of user needshave found that users exploit various aspects of digi-tal photography in usage scenarios such as sharingphotos with friends and retrieving photos of impor-tant events such as birthdays and weddings (K uchin-sky et al. 1999; Graham et al. 2002; Schiano et al.2002; Shen et al. 2002). B efore sharing, people typi-cally classify photos into those they wish to shareversus those they intend to leave “ in the shoebox.”T hus, there is a demand for powerful tools to helpusers organize, classify, and browse their collections.

T here are already many commercial and researchapplications supporting the organization of digitalphotos (ACD Systems; A pple Computer; B ederson2001; Canon; K ang and Shneiderman 2000; K uchin-sky et al. 1999). While our application shares fea-tures with some of them, our goal has been to makeorganizing and browsing photos simple and quick,while retaining scalability to large collections. Tothat end, we have concentrated on areas that improvethe overall experience without neglecting the mun-dane components of a digital photo organizationapplication. We facilitate organizing and viewinglarge photo collections by automatically dividingphotos into meaningful episodes or events, such as a

birthday party or a trip. T hough the events are auto-matically detected initially, the resulting boundariescan be manually adjusted if desired.

Our application presents photos in a verticallyscrollable light table which shows thumbnails of theuser’s entire photo collection, with markers indicat-ing the start of each event. A tree view for displayingevents and other attributes (people, places, etc.) canbe used to scroll the light table to a selected event, tosort photos by different categories, or to filter the setof visible photos to show only a particular category.

We fine-tuned our application by testing it withphoto collections from six different users each with500 to 1500 photos. In two of the collections, thephotographers organized the photos in directoriesrepresenting events. We used that information asground truth for evaluating our event detection algo-rithm. One collection contained photos taken by sev-eral photographers at the same event made us awareof issues with mixing scanned photos and photosfrom cameras set to different time zones. To test scal-ability, we used our application with the approxi-mately 4300 photos from all six collectionssimultaneously.

In the next section, we review several existingapplications and algorithms for organizing digitalphotos and compare them to our approach. We thendescribe the automatic techniques and user interfacemechanisms in our application. A fterwards, we dis-cuss experiences encountered during the develop-ment and use of the application. We conclude with adiscussion of future directions.

Simplifying the Management of L arge Photo CollectionsA ndreas G ir gensohn, J ohn A dcock, M atthew C ooper,

J onathan F oote & L ynn W ilcox

FX Palo A lto L aboratory, 3400 Hillview Avenue, Palo A lto, CA 94304, USA

{ andreasg, adcock, cooper, foote, wilcox} @ fxpal.com

A bstr act: With digital still cameras, users can easily collect thousands of photos. Our goal is to make organizingand browsing photos simple and quick, while retaining scalability to large collections. To that end, we created aphoto management application concentrating on areas that improve the overall experience without neglecting themundane components of such an application. Our application automatically divides photos into meaningful eventssuch as birthdays or trips. Several user interaction mechanisms enhance the user experience when organizing pho-tos. Our application combines a light table for showing thumbnails of the entire photo collection with a tree viewthat supports navigating, sorting, and filtering photos by categories such as dates, events, people, and locations. Acalendar view visualizes photos over time and allows for the quick assignment of dates to scanned photos. Wefine-tuned our application by using it with large personal photo collections provided by several users.

K eywor ds: Digital photo collections, automatic event detection, user-centered design.

Human-Computer Interaction -- INTERACT'03M. Rauterberg et al. (Eds.)Published by IOS Press, (c) IFIP, 2003, pp. 196-203

2 Related Work

There are numerous research and commercial appli-cations for viewing and managing collections of digi-tal photos. While our application shares features withsome of them, our goal has been to make organizingand browsing photos simple and quick, while retain-ing scalability to large collections. To that end, wehave concentrated on areas that improve the overallexperience without neglecting the mundane compo-nents of a digital photo organization application.

2.1 Light TableSeveral applications show photos in a grid-basedlight table (ACD Systems; Canon), either by reduc-ing the size of portrait photos or by adding extra mar-gins. PhotoMesa (Bederson 2001) shows all photosusing cached thumbnails of different sizes forimproved performance with a zoomable interface.iPhoto (Apple Computer) makes changing the imagesize in the light table very simple by placing a controlunderneath it to provide users with a choice betweenoverview and detail.

We optimized the light table in our application forfast scrolling by caching and pre-fetching imagethumbnails, and we offer three different image sizesto support the quick viewing of the entire photo col-lection. As a consequence, our application handlescollections of thousands of images smoothly. In con-trast to grid-based light tables, we pack photos intorows. To pack photos more tightly than iPhoto, we letadjacent rows overlap if they contain portrait photosin different areas such that the photos do not overlap.

Unlike other applications (e.g., ACD Systems),the photos in our light table can be re-ordered by thevarious categories (people, place, event) such that thespecific sub-categories are visually separated andstart with a marker. If a photo belongs to multiplesub-categories (e.g., a photo of several people), thephoto appears in each of the sections correspondingto an applicable sub-category.

2.2 Automatic Event DetectionMany applications use a hierarchical file system orset of photo collections as their main means forgrouping photos (ACD Systems; Apple Computer;Canon). In some cases, only the photos from a singledirectory can be viewed simultaneously (ACD Sys-tems). Our application is designed to process a user’sentire collection. We have emphasized time-orderedorganization of the collection by events, such asbirthdays or vacations, that are meaningful to thephotographer. Other researchers have also used pho-tos’ timestamps as input to automatic organizingalgorithms. The algorithms in (Graham et el. 2002;Platt et al. 2002) operate using an adaptive localthreshold applied to the inter-photo time interval.

Researchers at Kodak have developed an event seg-mentation algorithm based on clustering time differ-ences using K-means (Loui and Savakis 2000).

Our automatic event detection algorithm is basedon local self-similarity, as detailed in Section 3.2. In(Cooper et al. 2003), the algorithm was compared toGraham’s and Platt’s algorithms for automatic orga-nization of two collections. The photographer’sdirectory organization served as the ground truth forevaluation. While the other two algorithms had per-fect recall (1.0) in finding event boundaries, they alsodetected many additional boundaries (precision0.37). In contrast, our algorithm balanced precision(0.78) and recall (0.91) much better. Our automaticevent detector also estimates event boundaries atmultiple time scales. The interface allows the user tointeractively explore the automatically detected eventboundaries at the resolution of their choice.

2.3 Filtering and CategorizationAnother common feature among photo organizers ismetadata-based filtering to facilitate browsing andretrieval. PhotoFinder (Kang and Shneiderman 2000)supports form-based boolean queries. The FotoFilesystem (Kuchinsky et al. 1999) offers a tree view todisplay hierarchies of different categories such asevents, locations, people, or dates. For our applica-tion, we chose to offer simple filtering based on cate-gories shown in a tree view. Unlike FotoFile, thenodes in our tree view contain small representativeimages for the corresponding set of photos.

Our system is designed so that these categoriescan be assigned automatically if the supporting datais available, e.g., GPS data or reliable face recogni-tion (Kuchinsky et al. 1999). The tree view can alsobe used for a bulk assignment of categories simply bydragging a selection of photos on top of a tree node,in contrast to the drag-and-drop labeling in Photo-Finder (Shneiderman and Kang 2000) in whichlabels are attached to specific regions within a photo.

We also provide keyboard shortcuts for quicklyadding subjective ratings to photos. With a single keystroke, a user can rate a photo and advance to thenext. No other photo application has this feature.

Like MyPhotos (Sun et al. 2002) and PhotoshopAlbum (Adobe), our application provides a calendarview with photo thumbnails to support the browsingof photos. Unlike other applications, we optimize theuse of space in sparse calendar views by magnifyingthe days of the calendar where images are availableand by shrinking the areas where they are not. Also,instead of displaying a single thumbnail on each date,our calendar can indicate visually how many imagesfall onto a given day using a water-level icon. Wealso support changing photo dates via drag-and-droponto the calendar.

3 Photo Application

Our application’s interface presents three panes, asshown in Figure 1. The left pane allows users to viewand scroll the set of events using a tree view. Thispane also shows sets of people and place categories,as well as any other user-defined labels. By selectingthe corresponding tab, this pane also shows a calen-dar-based visualization of the collection’s events andphotos (see Figure 10). Both tree and calendar viewcan be used for navigating and filtering photos.

The right pane is a vertically scrollable light tablewhich, by default, shows thumbnails of the user’sentire photo collection in time order with markersindicating the start of each event. Using tabs, thispane alternately provides a larger view of the cur-rently selected photo. The categories in the left panecan be used to automatically scroll the light table to aselected event, or to filter the collection for photoswith a particular attribute (people, place, etc.).Attributes can be added to a group of photos with asimple drag-and-drop operation.

The bottom pane presents a photo tray that can beused to collect and arrange photos for viewing or forexporting, e.g., as a Web page. The photo tray can behidden to provide more space for the other panes.

The light table is optimized to efficiently show alarge number of photos. Our application offers theability to quickly scroll through the whole photo col-

lection to get an overview. Image thumbnails arecached on disk in the three supported sizes andloaded on demand when scrolling. All time-consum-ing operations such as loading photos at startup areperformed in the background so that users can imme-diately interact with their photos rather than havingto wait for the application to complete its processing.

The basic organizational building block in ourapplication is the event, such as a wedding, vacation,or family gathering. When acquiring new photos, ourapplication automatically groups photos into eventsbased on the photos’ time stamps. Users can bothadjust the granularity of the automatic event detec-tion as well as create new events manually and assignphotos to them.

3.1 Use ScenarioWe now present a scenario to highlight several of theimportant features of our system. Imagine that a userof our application went on a trip to Tokyo and tookpictures of friends. After returning from the trip, she

Figure 1: Event tree, light table, and photo tray views in the photo application.

Figure 2: Naming an automatically detected event.

imports the photos from the camera into our applica-tion along with photos from a company picnic thatare also still on the camera. Once the photos areimported, the automatic event detector finds twoevents, one for the company picnic and one for theTokyo trip. Figure 2 shows the user giving thedetected events meaningful names.

Now, the user wants to find some nice photos toshare with her friends. First, she selects several goodphotos in the light table with the mouse and ratesthem from the context menu (see Figure 3). Shecould also have rated them sequentially with the key-board by pressing the “+” and “-” keys to rate a photogood or bad and advance to the next photo.

After restricting the light table to show only thegood photos (as indicated below the light table inFigure 4), our user assigns the photos to categoriesrepresenting the people depicted in the photos. In thetree view, she creates a new person category “Cathe-rine” and drags several selected photos from the lighttable to the new category node to assign them to thatcategory (see Figure 4).

Finally, our user creates a new node in the treeview under Labels, “Tokyo for Catherine,” to group

the photos to be shared. She sorts the light table byperson by clicking in the People tree, and scrolls tothe photos of Catherine (see Figure 5). She selectsthose photos with a wire frame and drags them to thelabel (see Figure 6). Now, those photos can easily befound by choosing the “Tokyo for Catherine” node inthe tree. Those photos can be exported to a folder andshared, or copied to the clipboard and pasted into anemail message as attachments.

3.2 Automatic Event DetectionIn recent reports, researchers have found that orga-nizing photos by time significantly improves users’performance in retrieval tasks (Graham et al. 2002;Gargi et al. 2003). Intuitively, we often describe pho-tos by the events they document or the times theywere taken. While events are difficult to define quan-titatively or consistently, photographs from the sameevent are typically taken in close proximity in time.Example events include a birthday party or a trip toDisneyland.

Figure 3: Rating photos.

Figure 4: Drag-and-drop to assign person.

Figure 5: Selecting photos with wire frame.

Figure 6: Assigning a label by dragging photos.

As well, visually dissimilar photos can oftenbelong to the same event. For example, picturestaken at a wedding might contain outdoor and indoorshots from both a church and a separate receptionhall. Thus, content-based image similarity is oftenless useful for photo clustering or event detectionthan metadata. Unlike film, digital photographs typi-cally include metadata, such as the time and date, in astandard image header such as Exif (JEIDA 1998).

We adapt a similarity-based media segmentationalgorithm (Cooper and Foote 2001) to hierarchicallycluster photographs with similar (i.e., proximal)timestamps. This approach makes no assumptionsabout the distribution of the timestamps. The firststep is to sort the photos into time order. Assume theith photo in time order has timestamp ti. To automati-cally determine temporal structure in the photo col-lection, we construct a family of similarity matricesSK whose elements are calculated as follows.

(1)

By construction, the rows and columns areindexed by photo, in time order. Thus, the photoindex also runs along the main diagonal of each simi-larity matrix. Figure 7 shows two similarity matricescomputed from 100 digital photographs taken overthree months’ time. Dark blocks of high similarityalong the main diagonal indicate sequential clustersof similar photographs. Corners between the darksquares along the main diagonal indicate boundariesbetween two groups of photos.

In Figure 7, event boundaries appear as checker-boards along the main diagonal. To cluster the collec-tion into groups of similar photos, we calculate aphoto-indexed novelty score following (Cooper andFoote 2001). Peaks in the novelty score correspondto the boundaries between adjacent groups of photosthat each exhibit high within-group temporal similar-ity, and low between-group similarity, as measuredby Equation 1. The dashed lines in Figure 7 indicatethe location of the automatically detected clusterboundaries along the main diagonal of each matrix.

We locate peaks in the novelty score at eachscale, performing the analysis from coarse scale tofine (decreasing K). To build a hierarchical set of

event boundaries, we include boundaries detected atcoarse scales in the boundary lists for all finer scales.This procedure results in a list of event boundaries atmultiple resolutions. Ultimately, we present userswith the boundaries from a single resolution. Todetermine the “goodness” of the boundaries for eachtime scale, we calculate a confidence measure fromthe average within-class similarity and the between-class dissimilarity of the event clustering (Cooper etal. 2003).

Clustering the photos at multiple scales enablesflexible user interfaces that allow users to organizetheir photo collections at different time scales(indexed by the parameter K). We give users theoption to override our confidence measure for pick-ing the right granularity and to have the system detectevents at a coarser or finer resolution. Such a featureis important in situations were users might disagreeon the appropriate event boundaries for a given set ofphotos. For example, a week-long trip throughEurope might be seen as a single event by one userwhereas another user might consider the visit to eachcity to be a separate event. Because our approach ishierarchical, we could also show nested events in thetree view in such situations.

When photos are imported into the application,e.g., from a digital camera, events for them are auto-matically detected. To be able to add the importedphotos to existing events, events for all photos aredetected but they are only assigned to the newlyimported photos. Existing events are reused and newevents with generated names are added for photosthat do not belong into any of the existing events.

3.3 Light TableThe light table shows the whole collection of photosin a vertically scrollable window (see the right panein Figure 1). In contrast to grid-based light tables, wepack photos into rows. We let adjacent rows overlapif they contain portrait photos in different areas sothat the photos do not overlap.

We chose to offer three different image sizes forthe light table to provide users with a choice betweenoverview and detail. This allows us to precomputehigh-quality, anti-aliased thumbnails in the differentsizes for increased performance. We also preloadthumbnails centered around the current view to avoida delay when scrolling the light table a short dis-tance. We keep the smallest thumbnail size perma-nently loaded to provide a low-resolution proxy untilthe correct size thumbnail is loaded. As a conse-quence, our application handles collections of thou-sands of images very smoothly.

Depending on the criterion the light table issorted by, colored markers delimit the start of eachcategory (such as days or events) in the light table(see Figure 5 for a light table sorted by person). Incases where a photo belongs to multiple categories

SK i j,( ) e

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Figure 7: Similarity matrices for different intervals.

(e.g., a photo of several people), photos are shownseveral times in the light table, appearing in the sec-tion for each relevant marker. It is also possible to fil-ter the photos shown in the light table such that onlyphotos belonging to a particular category are shown(see Figure 8). The “Show all photos” button belowthe light table can be used to quickly see all photos.

Users can select multiple photos in the light tableand collectively rotate them, export them to a Webpage or a file folder, attach captions, or assign themto categories. Categories can be assigned eitherthrough a detailed dialog (see Figure 9) or by drag-ging the selected photos to a category node in the treeview (see Figure 6).

The photo viewer can be displayed in place of thelight table to view a single picture at a time atmedium (640x480) resolution. While in the photoviewer, operations can be performed on the currentphoto as if it were selected in the light table. In fact,the photo viewer and the light table are synchronizedsuch that the photo shown in the viewer is selected inthe light table. We also offer a full-screen slide-showview that provides transitions between photos andautomatic advancement to the next photo.

3.4 Rating PhotosPeople routinely separate printed photos into classesbefore sharing them with others, putting the bestshots in the family album and rest in the shoebox.With digital photos, this kind of classification is evenmore critical because there are more ways to shareand more photos (people shoot more with digitalcameras than their analog counterparts).

Our application helps people rapidly perform thistask by providing a single-key rating interface. Ini-tially all photos are rated “neutral”. By typing the

“+” key the selected photo is rated “good” and theselection moves to the next photo. Similarly thephoto can be rated bad by typing the “-” key. Thissequential quick rating method can be used either inthe light table or in the photo viewer mode. If photosare rated in the photo viewer, there is a brief pauseafter each rating to allow the user to see the appliedrating icon before moving on to the next photo. Aselected group of photos can also be rated good, bad,or neutral through the context menu (see Figure 3).Photos labeled as “good” (“bad”) are distinguished inthe light table with a small green (red) box. Photoslabeled “bad” are also faded out to make it easier toignore them. It is also possible to set the light table toshow only “good” photos.

3.5 Tree ViewThe tree view (left pane in Figure 1) gives the user away to visualize the photo categories and navigatethe light table in terms of those categories. The treeview has a sub-tree for each of several different cate-gories: Dates, Events, People, Places, and Labels.The first two of these categories, Dates and Events,are distinguished from the others in that a photo mayonly have a single date, and may only belong to a sin-gle event, while a photo may belong to any numberof People, Places, or Labels.

The currently selected sub-tree determines howthe photos are sorted in the light table. Clicking on atree node scrolls to and selects the first photo withthe selected value or tag. Subsequent clicks on thesame tree node advance the selected photo sequen-tially through the photos with that tag. The lowestexposed node along each path shows a thumbnail ofthe first image found under that node. The color ofthe thumbnail border matches the color of the corre-sponding tag label in the light table. Intermediatenodes along an expanded path do not show thumb-nails to save display space (see “2001” in Figure 1).Each node displays how many photos belong to thatcategory. If only some photos are shown in the lighttable, each node also displays how many of its photosare currently shown and is grayed out if none of itsphotos are shown. Nodes (other than date nodes) canbe renamed and new nodes can be created throughcontext menu items.

Figure 8: Filtering photos by person.

Figure 9: Adding a person to a photo.

All leaf nodes in the tree are drag-and-drop tar-gets. A selection of photos dragged from the lighttable to a leaf node (non-leaf nodes expand automati-cally when dwelled over) have the corresponding tagapplied. Each sub-tree contains a node labeled“Unknown”. Dropping a selection of photos onto thisnode erases all applied tags in that category.

The user can elect to show only photos that arebelow a given node through the node’s context menu(see Figure 8). By doing so, the light table will showonly photos with a particular tag, or from a particulardate. While filtering on a category value, the tree andlight table still function with the other category types.For example, the user can show only photos from1999, but then view those photos sorted by Places orPeople.

3.6 Calendar ViewThe calendar interface provides a way to visualizeand manipulate the photo collection through thefamiliar context of a wall-calendar. The calendar rep-resents the empty space between events in a way thatis lacking from the tree-based view. For each calen-dar date, the thumbnail of a representative image forthat date is displayed on the calendar. Alternatively,the calendar days can be filled with color to a hori-zontal level reflecting the number of images for thatdate, similar to a “water level” (see Figure 10). Wealso experimented with varying the color intensityand luminance to indicate of the number of imagesavailable on that date. A novel technique to maxi-mize the use of space in sparse calendar views is toexpand the days of the calendar where there areimages and shrink the areas where there are not.

Just like a date node in the tree view, selecting aday in the calendar scrolls the light table to the firstphoto from that date. Subsequent mouse clicksadvance the selection through the photos for thatdate. When the user wants to assign photos to anevent by hand, the calendar provides a natural inter-face for selecting time-contiguous groups of photos.By selecting a group of days in the calendar, the pho-tos on those days are selected, and can then beassigned to an event or other label.

The calendar view may also be used to applydates to undated or incorrectly dated images. Mostdeveloping labs provide scanning services that typi-cally generate undated images. A user may want tomerge dated images from digital cameras andundated images from film cameras or from digitalimages whose time and date information has beenremoved (e.g., by manipulating it with an image edi-tor). Photo dates may be set by dragging a selectedimage or images onto a date in the calendar. For deal-ing with cameras set to a different time zone or withan incorrect clock, we also offer a dialog to shift thetime for the selected photos by a specified number ofdays, hours, minutes, or seconds (see Figure 11).

4 Experiences

We received personal photo collections from six dif-ferent users each with 500 to 1500 photos. At variousstages of our application development, we met withsome of those users to discuss how they would like tosee their photos in our application. Several usersliked the fact that they finally could see their entirephoto collection in the light table rather than havingto open one folder at a time. The automatic eventdetection was also seen as a very useful feature.

One collection in particular challenged our viewsof a personal photo collection. It contained about1500 photos taken at a wedding weekend with sevendifferent digital cameras. Some of the photos weretaken with analog cameras and scanned in. Theclocks of the digital cameras were set to four differ-ent time zones. To see the different wedding activi-ties in order, the times for the photos had to beadjusted. Our feature for shifting time zones (see Fig-ure 11) turned out to be very useful for the digitalcameras. Because a granularity of one day was insuf-ficient to separate the wedding activities, we couldnot effectively use the calendar view to adjust the

Figure 10: Calendar view with water-level icons. The thumbnail mode is shown in the inset.

Figure 11: Shifting to a different time zone.

times for the scanned-in photos. Instead, activities inthose photos had to be identified visually so thatsmall groups of photos could be assigned the correcttime through the date change dialog. A more power-ful method for adjusting photo dates on a finer reso-lution is called for.

In observing people’s use of the photo applica-tion, we noticed interesting behaviors. For example,one user noticed several photos in sequence that weretaken from the same camera angle a few secondsapart. He was amused by rapidly switching throughthose photos in the photo viewer such that the move-ment between the photos appeared to be animated.Automatically created presentations of this sort couldbe a worthwhile added feature for our application.

The photo application is currently being preparedfor a larger use study from which we expect to gainmany additional insights.

5 Conclusions

In this paper, we described a photo managementapplication aimed at facilitating the management oflarge photo collections by implementing automaticorganization techniques, powerful but concise inter-face methods, and optimized data handling. Ourautomatic event detector produced results thatclosely matched the way in which users groupedtheir photos in folders. Our user interface providesseveral means for browsing, navigating, sorting, andfiltering photos that scale up well to collections con-taining thousands of photos.

Initial user response has been very positive andindicates that our application addresses some of theperceived deficiencies of existing applications.Future work will continue to refine and augment theapplication in response to user feedback and alsointroduce novel and compelling methods for creatingmedia objects for sharing and presenting photos suchas slide shows set to music.

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