VisualIDs: Automatic Distinctive Icons for Desktop Interfaces

1. VisualIDs:Automatic Distinctive Icons for Desktop Interfaces Nickson Fong ESC Entertainment Ulrich Neumann CGIT Lab U. Southern California J.P. Lewis CGIT Lab U. Southern California Ruth Rosenholtz Perceptual Science Group Massachusetts Institute of Technology FROM SIGGRAPH 2004

2. Introduction • Scenery: A distinctive visual appearance of objects in a place that allow us to recognize those objects and that place. • Our proposal is to generate visually distinctive icon(VisualIDs) automatically.

4. Psychology of Visual Search and Memory Subjects shown collections of hundreds or thousands of images are able to recognize the previously shown images with accuracies of 90% and more after only one brief viewing [Standing and Haber 1970]. Memory for briefly shown pictures is greater than that for words [Shepard 1967], and searching for a picture of a particular object among many is also faster than searching for the name of that object among other words [Paivio 1974]

5. Requirements for “Visual IDs” • Persistence • Identifier should be automatically assigned. • Scenery should be perceptually diverse. • Appropriate Complexity. • Detail vs. Scale

6. How Should an Icon Relate to its Data? • Most authors adopt the view that the data icon should based on meaningful characteristics of the data such as size, creation, and content. • But we take the contrary view that: scenery assignment can be fairly arbitrary

7. the role of scenery is not data visualization, rather it is to enable visual search and memory. • For example, we may select a good restaurant by reputation without knowing its appearance, but on a second visit we find it again having easily learned its appearance. Similarly, we cannot always guess the appearance of a book due to arrive in the mail from knowledge of its subject matter, but its somewhat arbitrary appearance is almost instantly learned and remembered once we see it.

8. The major point is that we do not need appearance to be consistently correlated with anything in order for us to recognize things. • Scenery is presented as a fait accompli and our visual brain is setup to rapidly learn this somewhat arbitrary mapping between appearance and content.

9. Similar Identify versus Similar Content • The filename represents the meaning of the file to the user. • We further believe that this meaning often cannot be formally determined from the contents of the data. • NIHfall, NIHfall_cover, NIHold.

10. Objects with similar (file)names should have similar visual identifiers

11. Synthesis Procedure • Identifying similar filenames using a clustering algorithm. • VisualID icons for unique filenames are generated using the shape grammar procedure. • The icon is arbitrarily assigned to the file by using a hash of the filename. • The prototype is then “mutated” to obtain the VisualID for remaining filenames in the cluster.

12. Name clustering • Pairwise string distance function: Levenshtein edit distance or Longest common subsequence. procedure one-level-cluster(newname) find the closest match among existing filenames if this distance < threshold then add newname to the cluster containing closest match else create new cluster with newname as its only member end

13. Shape Grammar • A shape grammar consists of a set of shapes (terminals), a set of markers (non-terminals), a set of productions that replace particular configurations of markers (and possibly shapes) with other configurations of shapes (and possibly markers), and a start configuration. • (N,T,M,P)

14. 1. M1 -> radial(M2,M3) (pick the radial production, marker 1 rewritten as M2, marker 2 rewritten as M3) 2. M2 ->radial(M4,M5), M4 ->line, M5 ->null (M2 now expanded as a second radial production with its own marker 1 replaced by the line terminal and marker 2 replaced by null) 3. M3 -> along-a-path(M6,M7), M6 -> line, M7 ->line (marker 2 of the top level radial expanded as along-a-path with its markers 1, 2 both replaced by the line terminal).

15. Shape Grammar This derivation generates a shape grammar M1 -> radial(M2,M3) M2 -> radial(line,null) M3 -> along-a-path(line,line) i.e. “an n-gon with n-gons around the perimeter with lines coming off of them, and inside the main n-gon a curving path with lines coming off it.”

16. Implementation details Around-a-spiral The spiral algorithm is for( i=0; i < len; i++ ) { theta = 2*pi*float(i) / ppr; r = 0.15 * sqrt(theta); x = r * cos(theta); y = r * sin(theta); }

17. Scheme language shape-grammar pseudocode.

18. Mutation • In the construction described here “mutation” is relatively easy – the generated grammars can be walked through and one or more of the embedded parameters changed. • Two examples of the results of mutation

19. Results

21. Experimental Validation(1/2) • Study 1 In the first study, users were asked to find files, specified by filename, in a “file browser” displaying files in a simulated folder, using either distinctive (VisualID) icons or generic (plain document) icons. Each user searched for 2 files, 3 times each, for each type of icon. This repeated search task tests a combination of visual search and short term icon memory: users quickly learn the association between filenames and VisualIDs and can thus use the VisualID to aid in search.

22. Experimental Validation(2/2) • Study 2 Users viewed a 5x4 array of cards, with either a VisualID or generic icon on each card, and a file name beneath (Fig. 8). Each user saw only one type of icon (VisualIDs or generic). When the user clicked on a pair of cards, this revealed the name of a country behind each card. The country thus plays the role of the “content” of the file. The user’s goal was to find all 10 matching pairs of countries. For additional training, the user was then asked 25 additional questions such as “Which country is north of the USA? (Click on the pair.).” The next day, users performed a second set of tasks. First, they were shown a series of icons, and asked to pick the associated country in a multiple choice question.

23. Experimental Results

24. Discussion and Future Work • Coexistence, not Replacement. • Scaling issues. • Evidence indicates that search for a picture among pictures is better than search for a word among words in this regard (e.g. [Paivio 1974]). • Branding. • Software vendors may object to having their branding logo (e.g. the “W” icon identifying a Word file) replaced with something more unique. • Future Direction: Visual Search Interfaces. • Visual identifiers might allow query-by-sketch to operate on other types of data. • A search that returned the N closest matches could be used in conjunction with other constraints (file type, date, ...)