Motivating Annotation for Personal Digital Photo Libraries:
`Lowering Barriers While Raising Incentives
`
`Jack Kustanowitz
`University of Maryland
`College Park, MD 20742
`+1 (301) 405-1000
`jkustan@umd.edu
`
`Ben Shneiderman
`University of Maryland
`College Park, MD 20742
`+1 (301) 405-1000
`ben@cs.umd.edu
`
`ABSTRACT
`The value of personal digital photo libraries grows immensely when users invest effort to annotate their photos. Frameworks
`for understanding annotation requirements could guide improved strategies that would motivate more users to invest the
`necessary effort. We propose one framework for annotation techniques along with the strengths and weaknesses of each
`one, and a second framework for target user groups and their motivations. Several applications are described that provide
`useful and information-rich representations, but which require good annotations, in the hope of providing incentives for
`high quality annotation. We describe how annotations make possible four novel presentations of personal photo collections:
`(1) Birthday Collage to show growth of a child over several years, (2) FamiliarFace to show family trees of photos, (3)
`Kaleidoscope to show photos of related people in an appealing tableau, and (4) TripPics to show photos from a sequential
`story such as a vacation trip.
`
`1. INTRODUCTION
`People tend to spend very little time annotating their personal photos. How many family photographers are diligent enough
`or have enough time and energy to get a roll of film back from being developed, go over the pictures, and put them into
`albums, instead of just sticking the pictures in a shoebox? How many people go through their digital photos and give each
`one a unique file name in an appropriate directory instead of leaving them in the default directory created by the camera
`software? Not many [9].
`
`On the other hand, the two most useful features for coping with digital photos – chronological sorting and displaying large
`numbers of thumbnails – are already available. And users’ responses when asked about more advanced features reflect
`some interest, but indicate that they would not miss them if they were absent [22].
`
`As a result, more and more people find themselves with thousands of digital photos with little organization and little utility,
`and they are resigned to gaining no more benefit or enjoyment from them than the thousands of printed photographs stored
`in shoeboxes around the house.
`
`Annotation has the power to transform this semi-random collection of photos into a powerful, searchable, and rich record of
`people’s lives and experiences [4]. Some of the opportunities that come from annotated pictures are described in the
`Related Work section below. But the main question remains: What motivates people to annotate photographs, and how can
`we as developers better entice people to spend valuable time adding meta-information to their photo collections?
`
`Part of the problem is that users themselves do not understand what they can do differently once the pictures are annotated
`[9]. In a study of information capture, 10 modes of capture were discussed [3], half of which could only be done in the
`digital realm. The existence of these exclusively digital modes provides good motivation for finding ways to take advantage
`of them in applications that utilize the unique capabilities of the digital medium.
`
`This paper describes several techniques of annotation currently available, in order to build a good understanding of what is
`possible and what the strengths and weaknesses of some of the annotation technologies are. We describe a framework for
`these annotation techniques to better understand who does annotation and for what purposes, towards the goal of lowering
`barriers to annotations by making the interface easier, while increasing the incentives by enabling novel, automatically
`generated presentations. Finally, we focus on four applications that are made possible by well-annotated data, in the hope
`that they provide motivation for users to spend time adding rich metadata to their digital photograph collections.
`
`2. TYPES OF ANNOTATION
`The goal of photo annotation is to create semantically meaningful labels and associate them with the photo. Captions
`(freestyle text like “Mom & Dad at their 25th anniversary”) can be used for full-text searches and enable storytelling and
`dialogue-based sharing, but do not allow meaning to be inferred. Categorization (assigning a word from a finite vocabulary
`to a picture, like “sunset”, or “Paris”), is better in the sense that searches are now possible on a finite set of words, but it still
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`lacks the ability to bestow meaning (is Paris a place or the name of a person?). Without assigning independent semantic
`meaning to the labels, there is no way to ask for all people appearing in the photo collection, for example.
`Annotation can also be in a non-text form, such as a sketch that abstracts the photo’s contents for “looks like” searches
`where the input is drawn instead of typed [7]. For audio annotation, a melody line could be used, in order to later search an
`audio collection by humming into a microphone. We focus on text, the predominant and most relevant annotation for
`interacting with photographs.
`Annotation techniques can be grouped into three categories: Manual, Semi-Automated, and Automated. Each has
`advantages and disadvantages, and we will discuss them in order of increasing automation
`
`Table 1. Annotation Techniques
`
`Human Effort
`
`Machine Assistance
`
`Manual
`
`Semi-Automated
`
`Automated
`
`Add structured annotations, with
`sufficient semantic information to be
`useful for retrieval.
`
`Add freestyle annotations or captions.
`Potentially work with machine’s
`output in an iterative fashion.
`Verify machine’s accuracy and make
`corrections as needed.
`
`Save annotations in a database.
`
`Parse human-entered captions and extract
`semantic information.
`
`Add structured annotations using GPS,
`context, or recognition technology.
`
`2.1 Manual Annotation
`Most commercial software packages (Adobe Photoshop Album, ACDSee, Picasa, etc.) and web-based photo services (such
`as Yahoo, Snapfish and Ofoto) use manual annotation. They include the ability to set up a hierarchical list of categories
`(where depth of the hierarchy depends on the package), and add photos to those categories. They are an improvement over
`a regular file system in that a photo can exist in more than one place in the hierarchy. They also allow the addition of free-
`text captions, which while useful for online albums, can be difficult to search or to use for semantic extraction.
`Manual annotation can provide the most accurate information, but it is also the most time intensive. The “group
`annotation” discussed below may mitigate the time requirement, but users still need to make one or more decisions for each
`picture. We describe two applications that support this kind of manual annotation, although there are many others as well.
`Adobe PhotoShop Album is organized around a “Photo Well” into which photos are dropped when first imported. Users
`can define “Tags”, which are set up as a 2-level hierarchy, where either the tag name (“Al”, “Esther”) or the category
`(“Family”) can be dragged and dropped onto a picture in order to classify it.
`PhotoShop Album also supports dragging and dropping in the other direction (picture onto tag/category), as well as
`multiple select drag and drop in either direction. A slider bar on the bottom of the screen allows fine control of the size of
`pictures in the photo well, allowing for selection and group annotation of even hundreds of pictures.
`
`Figure 1. Adobe Photoshop Album
`
`Figure 1. PhotoFinder
`
`
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`PhotoFinder takes photo annotation one level deeper. Users can drag person names or other terms from a scrolling list and
`drop them onto a photo. Caption text can be placed on or under photos. PhotoFinder also associates annotations with
`coordinates within each photo [25]. This level of resolution potentially allows queries of “Bill next to John”, for example.
`The downside is that the annotation process requires more attention to detail, since it is necessary to notice where on the
`photo the annotation is placed. Additionally, group-annotations cannot benefit from coordinate information, since
`presumably the positioning of elements will be different in each picture in the group.
`
`2.2 Semi-Automated Annotation
`Semi-Automated Annotation starts with a manual process, presumably one that is easy and natural like voice annotation. It
`then goes through the manual annotations and extracts higher-quality, searchable metadata, which it then re-associates with
`the picture.
`For example, SmartAlbum [29] assumes speech annotation of the photos has been done, and then proceeds to extract
`semantic information from the WAV file. It is limited by the accuracy of speech recognition, and will need to have some
`form of manual error correction, as the annotation algorithm uses heuristics to guess at event boundaries but is not
`guaranteed to succeed all the time.
`Alternatively, the human-created caption can be parsed using a “Common Sense Knowledge Base” such as CYC [15] or
`OMCS [27], leveraging the implied context of a photo (i.e. recognizing a bride could let the engine infer that the photo is of
`a wedding) to create a semantically meaningful annotation for future use. [17]
`The MiAlbum system [32] uses a feedback mechanism to iteratively improve annotations as part of the search process. The
`first search returns random results, which users grade for relevance, thus improving future searches. With each search and
`with extended use of the system, the quality of the annotations improves in an iterative fashion. [31]
`Using the Mobile Media Metadata framework [34], camera phone users can annotate their photos while still in the physical
`environment of the photo, rather than waiting until they return to a desktop PC. Annotation is first done automatically
`using date and location information, and then users are given a chance to interact with the system’s automated annotations,
`selecting the correct choice from a drop down list of pre-populated answers in an XHTML browser on the phone.
`
`2.3 Automated Annotation
`Automated Annotation has the clear advantage that it happens with no user intervention, making it an attractive candidate.
`However, even in an ideal world, with perfect face recognition and shape detection, a computer will not be able to apply
`event labels, such as “Bill’s 21st birthday party”, or other heavily context-dependent annotations. Still, it behooves the
`designer to automate when possible, in order to minimize the amount of manual attention that is required.
`The most basic type of automated annotation is done inside the digital camera by applying the time stamp, which newer
`cameras prompt users for if it is not set. In fact, date and time have been shown to be the most important piece of metadata
`to record, as 92% of the subjects in a recent study had a specific time association with certain photographs [12].
`An extension of this idea is to include a GPS receiver in the camera, and include a location stamp along with the time
`stamp. Microsoft distributes the WWMX TrackDownload application [36] that will use GPS data saved on a regular GPS
`device (and associated with a particular date/time) to stamp photos after they have already been downloaded to a PC. GPS
`receivers are attractive, but their inability to do location sensing in buildings requires some workaround, such as recording
`the last known location.
`Another technology senses location based on the strengths of packets on an IEEE 802.11b wireless Ethernet network [14].
`And finally there are proposals for getting geographic location by synchronization with cell phones that would be carried by
`the photographer. When the photographer uploads photos to a desktop or laptop that is Internet connected, a link to the cell
`phone company database would produce time-synchronized location data.
`The methods mentioned above use technologies with low susceptibility to errors, and thus with a high accuracy rate. Other
`approaches to automated annotation use methods that are more error-prone, but potentially yield more interesting data.
`Some applications choose to use surrounding text as a way to generate extra metadata for photos [30]. Google, for example,
`uses this technique to automatically index vast numbers of images on the web in an automated fashion [11].
`The Shoebox project [18] uses comparison of feature vectors (color, texture, location, and shape) to index images in an
`automated fashion. Advanced algorithms also exist for face recognition [38], and these can also be used to generate
`conjectures as to the contents of a photograph [37].
`Aria [16] is a tool that links annotated photos to an email client. In addition to offering suggestions for relevant photos
`during the composition of an email, it is capable of adding annotations to photos in the collection as the email is written,
`based on keywords and information from a common sense database. [15][27]
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`As with machine transcription, the user can stop at that point, and decide to live with whatever the success rate might be,
`but it is usually worthwhile to take a second pass and fix errors or add additional information. In the best case, automated
`annotation saves most of the work and requires only touch-ups, but in the worst case it can require more time to “edit” the
`result than it might have been to just do manual annotation from the beginning.
`
`3. A FRAMEWORK TO DESCRIBE MOTIVATIONS FOR ANNOTATION
`Since photo annotation is used in widely varying contexts for very different purposes, categorizing by user groups, helps
`target applications. We divide target audiences into "Self, Friends & Family, Colleagues & Neighbors, and Citizens &
`Markets" [24] and then discuss each category in terms of what barriers it presents to the creation of quality annotation, and
`what incentives can be offered to lower those barriers.
`
`Audience
`Self
`
`Family and
`Friends
`
`Colleagues and
`Neighbors
`
`Citizens and
`Markets
`
`Table 2. Motivations for Annotation
`
`Description
`Annotation of photos located on
`unshared PC
`Photos are shared, and family &
`friends benefit from annotation
`work
`Local community projects
`
`Large payoff to work ratio, as one
`person’s work is used by large
`numbers of people
`
`Motivation
`Orderly personality, plan to use search &
`visualization tools
`Recognition and appreciation, social value
`of sharing easy access to photographs
`
`Improve quality of community, same as
`volunteering for other public projects
`
`Financial reward, or recognition and
`industry credibility if unpaid.
`
`If annotation is being done for a single user, the barriers for annotation are greatest. Laziness or just not having time can
`cause annotation to be put off indefinitely, as other more pressing tasks take precedence. The benefits need to be clear, and
`it is for this user that many of the most creative applications have been created. Unfortunately, most people who annotate
`for the other audiences begin by annotating for themselves, and barriers at that early stage of learning the technology can
`prevent users from progressing to the stage of annotating for larger audiences.
`Family and Friends add an additional motivation of an external audience; annotated pictures have more value in the larger
`group. Members of this group can more easily share, locate, and view photos that have shared emotional value for all of
`them.
`On the community level, annotation increases the value of group photos, whether they are historical, biographical, or
`projective (plans for a new building or park area, for example). It might be accepted as a way of contributing to the group,
`comparable to volunteering to serve on a board of directors or planning committee.
`Annotation for a worldwide audience holds the great promise of one person’s work benefiting millions. The World Wide
`Web is the best example of this kind of technology, and there is currently much discussion on engines that will search that
`medium. For photos, free text search is not an option, and so the importance of annotation is correspondingly greater.
`Members of the world community can be motivated by profit (it may be someone’s job to perform annotations), in which
`case the job of the software developer is to make them as productive as possible in their work, and make the process stress-
`free and enjoyable.
`Additionally, since the target audience is so large, annotators gain widespread recognition and credibility by doing good
`work that can be used by many people, even if they are not receiving direct compensation for their work. The best model
`for this is the Open Source initiative [20], in which the individual gets public credit on a web site and can demonstrate
`publicly visible work as a reference when applying for jobs or consulting positions.
`Individuals are the greatest challenge for the software designer, since without being strongly motivated to add annotations,
`they will favor more pressing projects. Therefore, especially in this case, the external motivation needs to be highest, and
`barriers need to be removed whenever possible. Specifically, the task of annotation needs to be:
`1. Fun – since the task is being done voluntarily
`2. Effective – i.e. result in a valuable product.
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`The absence of either or both of these ingredients relegates annotation to a “chore”, which will get put off as long as
`possible and ultimately not get done.
`Very little has been written about the “Fun” component, although designers do strive for GUIs that are engaging and easy to
`use. Even though fun is an inherently subjective quantity, for a non-essential tool, it is imperative.
`To be effective, annotation needs to facilitate the goals of photo sharing in general, which can be translated into the digital
`realm as: Remote Sharing, Sending, Archiving, and Co-Present Sharing [9]. It also needs to be powerful enough to enable
`various retrieval technologies, some of which are described below.
`In designing annotation applications for the individual that 1) successfully remove barriers due to task difficulty, and 2) add
`motivations based on powerful and compelling applications, the designer encourages the individual to get started in a new
`area of technology, and increases the chances that the individual will proceed to create for larger audiences to the benefit of
`all.
`
`4. APPLICATIONS
`For many of the audiences mentioned above, motivation is critical, since there are no profit or job rewards associated with
`completing the task, and recognition comes not as a result of the annotation itself, but what the annotation enables.
`Following are several collages of photos, each organized slightly differently. What unifies them is the attribute of automatic
`generation – they are all views of potentially hundreds or thousands of photographs that can be automatically generated
`using existing annotations. The hope is that by creating applications that assume quality annotation has been done, these
`compelling presentations would in turn motivate users to create the annotations in the first place..
`
`4.1 FamiliarFace
`We wrote FamiliarFace as a prototype application to illustrate automatic generation of a pictographic family tree. The user
`chooses people from the list of people in their annotations, and defines relationships (parent, child, spouse, etc.) between
`them. The program then goes to the larger picture collection and generates a collage of thumbnails.
`
`Figure 2. FamiliarFace
`Finally, it pulls a picture from the collection that has been marked as “Favorite”, and uses that as the primary picture (on
`top of each window, on a green background). Controls are provided for narrowing the focus either by generation (view 1, 2,
`3, etc. generations), or by calendar (view from June 1999 – April 2000).
`With minimal work (defining relationships), entire family trees spanning dozens of people and several generations can be
`built. With the ability to read GEDCOM [10] files (XML standard for genealogy), even the genealogical data itself can be
`imported, making the entire process automatic.
`With any attempt to show large numbers of pictures on a single screen, space is an issue. The scroll bars shown above
`make an effort to allow for more photos to be shown; another solution involves allowing zooming out to see an entire tree
`with hundreds of nodes, and zooming in to view a single photo [2].
`A single user might use FamiliarFace to quickly find pictures of a certain person, and identify weaknesses in the collection
`(people with very few pictures taken of them within a specific time frame) in order to focus more on them in the future. In
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`anticipation of a family gathering, an elaborate collage could be generated spanning multiple generations and including
`hundreds of pictures filtered by quality.
`A company organization chart could be put together as well, treating the corporate hierarchy as a sort of family. This would
`be useful in letting employees get a look at colleagues in other settings, allowing for a stronger social and empathetic bond
`to be formed in the workplace. On the level of citizens and markets, a FamiliarFace type of interface could provide broad
`overviews of photo collections with some implied hierarchy, like decades of an artist’s life or accepted periods of time in
`history (Renaissance / Classical / Modern, for example). Users could browse huge collections and choose photos for
`personal or professional use, possibly paying royalty fees once they find the right picture for their purposes.
`
`4.2 Birthday Collage
`Figure 4 is a manually generated collage: It is a series of pictures of an infant taken at regular intervals, and assembled for
`sharing with friends and family. Just using the date stamp from the digital camera, such a collage could be automatically
`generated easily, using a random choice of picture from each month. With some additional “quality of photo” annotations,
`the best photos could be filtered out and a complete work generated with minimal user effort.
`
`Figure 3. Birthday Collage
`The birthday collage is a classic “friends and family” application, and it extends easily to the realm of colleagues and
`neighbors. For example, construction jobs, community gardens and flowers, and laboratory experiments are all “projects”
`that grow change at small intervals and whose arrival at larger intervals is marked. A good example at the citizens &
`markets level is the 100 year history of photos intermixed with Life Magazine covers to provide historical context, created
`by John David Miller at Intel.
`
`4.3 Kaleidoscope
`The Family Kaleidoscope is a prototype of an organized view of a set of photographs. It starts with a single individual in
`the center, and proceeds outward in concentric rectangles, each of which is related in some way to a previous level.
`
`Figure 4. Kaleidoscope
`A control panel on the left controls which picture(s) are selected on a given level. The model assumes indication of
`“Favorites” within the annotations, to narrow down the choices from the complete set of photographs in the collection.
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`Beyond the Friends & Family application shown above (Figure 4), a similar program could include a “six degrees of
`separation” feature that would tell you at a glance who has appeared in photos with whom. For colleagues, conference
`highlight pictures/posters, showing connections between people and even between people and products or booths could be
`generated to summarize the conference and to market the following year’s event.
`And on the mass-market level, Hollywood collages of actors and which movies they’ve starred in together could be
`dynamically created for sale on fan web sites, or as an added feature on movie-oriented web pages.
`
`Figure 5
`Figure 6 shows a recent implementation of the Kaleidoscope idea, in which a single primary image is surrounded by groups
`of related photos. The kaleidoscope effect in this case conveys a two-level hierarchical relationship between the primary
`photo and the surrounding groups of photos. All photos and regions dynamically resize to maintain optimal thumbnail size
`as the center photo is moved or resized, and as the whole area is resized.
`
`4.4 TripPics
`TripPics is a prototype of an application for displaying pictures that tell a sequential story. In this case, the pictures
`describe a trip through Italy, with major parts of the trip set off by separate boxes.
`
`Figure 6. TripPics
`In this screenshot, each “event” along the trip gets a single representative picture, automatically selected based on the
`“Favorite” tag, although the single picture could easily be replaced by the collage shown in FamiliarFace, to get a better
`idea of the entire collection of pictures from each part of the trip. The color saturation of the blue background is
`proportional to the number of pictures in that subset of the collection.
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`The pictures are shown on a white background, but they could also be superimposed on a semi-transparent map of the
`region traveled, with arrows or other indication of direction of travel.
`A single user might use TripPics to quickly generate an album of a trip, which otherwise could take a lot of time. This
`album could be shared with friends and family, and provide more information than just a collection of pictures, even if free-
`text captions had been added. On a more global scale, different TripPic collages could be made public on a web site for
`people considering a similar trip, and could become integral parts of guidebooks to help evaluate destinations and sequences
`of travel.
`
`5. RELATED WORK
`Many other digital library applications that require quality annotations have been proposed. The Personal Digital Historian
`(PDH) project [23] lays out photos on a round tabletop, and provides both an annotation engine and a novel display that
`takes advantage of the annotations to perform grouping, sorting, etc.
`Fotonotes.net [8] is a web-based wiki-style interface that lets users create their own online collection of photos. Instead of
`annotating the photo as a whole, users create regions within the photo and attach story-style captions to each one. For
`example, in a picture of a living room, one caption could describe a painting and another a chandelier. Each region within
`the picture is then independently addressable, and can be viewed by URL as a cropped version of the original.
`In the category of shared photos, Microsoft has an ambitious research project called WWMX, the WorldWide Media
`exchange [35]. It uses the GPS location stamp in the photo’s metadata to associate a picture with a specific location, and
`then show a map of the world (zoomed to arbitrary detail) with pushpins of various sizes representing the concentration of
`pictures at that location.
`An image browser that groups photos by visual similarity has been proposed [21], with mixed results. Some users
`appreciated the novel grouping (that would group pictures of sunrises, for example), but others were disoriented because the
`similar pictures seem to merge, making it harder to choose a specific picture.
`When considering annotations done by a large group of people, questions of privacy, trust, and malicious users need to be
`addressed. In a controlled audience (a family or community), these issues are more manageable [13], as in the PhotoHistory
`of SIGCHI [26], at which annotation of a 20-year photo history was made public, with no malicious or inappropriate
`annotation. Even so, there were several requests to remove annotations and pictures due to privacy concerns.
`On a larger scale, Wikipedia [33] is a large (6000 contributors working on 600,000 articles in 50 languages as of this
`writing) public annotation project, and does not seem to suffer from a large degree of malicious intent.
`There are also several online review clearinghouses (Amazon.com book reviews [1], Epinions [6], etc.) where users can
`make annotations that are visible to the world. In these systems, some rules are explicit and are controlled by a moderator,
`and others are implicit and will result in peer criticism or reduced peer support.
`Most of the online photo development companies (Snapfish [28], Ofoto [19], etc.) let the photo’s owner to add captions.
`These captions are viewable (although often not searchable) by whomever the owner invites to share the pictures.
`Ebay [5] is an example of a peer-to-peer financial transaction system, in which trust is critical. They have implemented a
`peer-review system, in which every seller has a rating that is a function of previous transactions. Sellers are highly
`motivated to provide quality goods and services, lest they be branded with a negative rating, which could affect future sales.
`While sellers with bad ratings can create new identities and “clear their name”, they will also lose any positive rating as
`well as the sales history that shows their experience and reliability. This kind of system could be adapted to a large
`repository of digital photos to ensure quality and mitigate the potential for malicious use.
`
`6. FUTURE WORK
`A major challenge facing designers of personal digital photo libraries is how to lower the barriers for manual annotation.
`When would users prefer to annotate? While the photo is being taken? At download time? At "share" time? While users
`are assembling an album? Each has its advantages and disadvantages: When users are downloading the pictures they are
`engaged in a basic annotation exercise (choosing a file location), so that might be a good time. On the other hand, during
`sharing, users are communicating about the photos, and it seems reasonable to record descriptions then, for later semi-
`automated annotation.
`Automated annotation depends on some advances in feature recognition, but a first step might be to recognize categories of
`objects (animals vs. people vs. scenery), and get that level of recognition into a consumer product. Perhaps even a limited
`automated annotation would spark an understanding of what full recognition might provide, and cause more interest in the
`process. The “cool” factor (wow, it knows which pictures are of my cats!) would also motivate people to put more work into
`annotations.
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`This paper discusses annotation of digital photo libraries, but photos are just a subset of the media, including voice, music,
`video, etc. These ideas could be extended beyond photos, and the models reworked to cover a broader spectrum of personal
`and public media libraries.
`Finally, research should be done on what constitutes a “fun” interface, which might vary among applications and cultures.
`For individual annotation and use, it needs to have a visceral appeal that will make users excited to perform an otherwise
`optional task, and satisfied with quick and meaningful results.
`
`7. ACKNOWLEDGMENTS
`
`The authors acknowledge Adobe Corporation for funding research that contributed to this work. They would also like to
`thank Greg Elin for his feedback and comments.
`
`8. REFERENCES AND CITATIONS
`[1] Amazon.com, http://www.amazon.com, Accessed on January 16, 2005.
`[2] Bederson, B.B., Quantum Treemaps and Bubblemaps for a Zoomable Image Browser (2001). Proc. ACM Conf. User
`Interface and Software Technology (UIST 2001), 71-80. ACM Press, New York.
`[3] Brown, Barry A. T., Sellen, Abigail J., O’Hara, Kenton P. (2000). A Diary study of Information Capture in Working
`Life. Proc ACM CHI ‘2000, 438-445. ACM Press, New York.
`[4] Chalfen, R. (1987). Snapshot Versions of Life, Bowling Green State University Popular Press, Ohio.
`[5] Ebay.com, http://www.ebay.com, Accessed on January 16, 2005.
`[6] Epinions.com, http://www.epinions.com, Accessed on January 16, 2005.
`[7] Flank, Sharon (2002). Multimedia Technology in Context. IEEE Multimedia 9(3): 12-17.
`[8] Fotonotes, http://www.fotonotes.net, Accessed on January 16, 2005.
`[9] Frohlich, David, Kuchinsky, Allan, Pering, Celine, Don, Abbe, and Ariss, Steven (20