Experiments in Objet Trouve Browsing
Simon Colton, Jeremy Gow, Pedro Torres, and Paul Cairnsy
 Department of Computing, Imperial College, London, UK.
y Department of Computer Science, University of York, UK.
Abstract. We report on two experiments to study the use of a graphic
design tool for generating and selecting image lters, in which the aesthetic
preferences that the user expresses whilst browsing ltered images
drives the lter generation process. In the rst experiment, we found evidence
for the idea that intelligent employment of the user's preferences
when generating lters can improve the overall quality of the designs
produced, as assessed by the users themselves. The results also suggest
some user behaviours related to the delity of the image lters, i.e., how
much they alter the image they are applied to. A second experiment
tested whether evolutionary techniques which manage delity would be
preferred by users. Our results did not support this hypothesis, which
opens up interesting questions about how user preferences can be intelligently
employed in browsing-based design tools.
1 Introduction
The Objet Trouve (Found Object) movement in modern art gained notoriety by
incorporating everyday objects { often literally found discarded in the streets {
into visual art and sculpture pieces. This is a two stage process, whereby the
original object is rst found, and then manipulated into a piece of art. This
process is analogous with certain practices in computer-supported graphic de-
sign. In particular, both amateur and expert designers will often nd themselves
browsing through libraries of image lters; or brush shapes; or fonts; or colour
palettes; or design templates; etc. Once they have found some possibilities, these
are pursued further and manipulated into a nal form. This analogy with Objet
Trouve methods is most pronounced in the eld of evolutionary art, where artis-
tic images (or more precisely the image generating processes) are evolved, e.g.,
[5]. Here, the software initially leads the user, through its choices of processes to
employ and the way in which it combines and/or mutates those processes as the
session progresses. However, as the user begins to exert their aesthetic prefer-
ences through their choices, the software should enable them to quickly turn their
found processes into a nal form. We investigate here the behaviour of \amateur
creators" [2] when using such design software. Our motivation is to ultimately
build software which acts as a creative collaborator in design processes.
We present here the results from two experiments where participants were
asked to undertake graphic design tasks using a simple design tool which allows
users to browse and select image-ltered versions of a source image in an Objet
Trouve fashion. Various techniques may be used to supply new lters on demand.
As described in section 2, our tree based image ltering method enables evolu-
tionary, database lookup and image retrieval techniques to be used in providing
238
Fig. 1. Filter tree with transforms
in blue circles: (A)dd
colour, (C)onvolution, (I)nverse,
(M)edian and (T)hreshold; and
compositors in red squares:
(A)nd, (F)ade, (M)in and (O)r.
Image inputs are in green diamonds.
An original image and
ltered version are shown.
a user with new lters. We incorporated six such techniques into a very pared-
down user interface which enables the user to undertake simple graphic design
tasks. As described in section 3, the rst experiment was designed to test the
hypothesis that employing the user's current choices to intelligently determine
what to show them next is more eective than a random selection method. The
data revealed that users ascribe on average a higher score to designs produced
by the intelligent methods than produced randomly. In addition, by studying
user preferences towards the six generation methods, we hypothesised certain
user behaviours, largely involving their preferences towards more conservative
image lters, i.e., ones with high delity which don't radically change the origi-
nal image. Studying these behaviours enabled us to design a second experiment
involving evolutionary techniques where evolved lters are supplemented with
other lters. As described in section 4, this experiment tested the hypothesis
that choosing the supplementary lters to manage the overall average delity of
the lters would be more eective than choosing them randomly. The data did
not support this hypothesis, which opens up interesting questions about how
to analyse the behaviour of a user to improve the quality of the content they
are shown as their browsing session progresses. In section 5, we suggest more
intelligent methods for future Objet Trouve design approaches.
2 Image Filtering
An image lter such as blurring, sharpening, etc. manipulates the bitmap infor-
mation of an original digital image into bitmap information for a ltered version
of the image. We represent image lters as a tree of fundamental (unary) image
transforms such as inverse, lookup, threshold, colour addition, median, etc., and
(binary) image compositors such as add, and, divide, max, min, multiply, or,
subtract, xor, etc. In the example tree of gure 1, the overall lter uses 7 trans-
form steps and 6 compositor steps, and the original image is input to the tree
7 times. Using this representation, image lters can be generated randomly, as
described in [3]. We used such random generation to produce a library of 1000
hand-chosen lters, compiled into 30 categories according to how the ltered
images look, e.g., there are categories for lters which produce images that are
blurred, grainy, monotone, etc. The time taken to apply a lter is roughly pro-
portional to the size of the input image multiplied by the size of the tree. Over
the entire library of lters, the average number of nodes in a tree is 13.62, and
the average time { on a Mac OS X machine running at 2.6Ghz { to apply a lter
to an image of 256 by 384 pixels is 410 milliseconds.
239
2.1 Filter Generation Methods
As described in the experiments below, we investigate how best to supply a user
with a set of novel lters (N) given a set of lters (C) for which they have already
expressed an interest. We have implemented the following methods.
 Database methods. These two methods use the 30 hand-constructed cate-
gories from our image lter library. The Random From Category (RFC) method
supplies lters for N which are chosen randomly from the library. To do this,
rstly a category is chosen at random, and then a lter is chosen at random from
the category. Given that some of the categories have up to 100 lters in them,
we found that choosing evenly between the categories gave the user more variety
in the lters shown than simply choosing from the 1000 lters at random (which
tends to bias towards lters in the most popular few categories { which can look
fairly similar). Whenever a lter has been shown to the user, it is removed from
a category, so it will not be shown again, and if a category has been exhausted,
then a new one is chosen randomly. The More From Categories (MFC) method
takes each lter in C with an equal probability, nds the library category from
which it came and then chooses a lter from this category at random to add to
N. As before, lters which have been shown to the user are removed from the
category. Exhausted categories are re-populated, but when a lter is used from
the category, the lter is mutated (see below), to avoid repetitions.
 Image retrieval methods. An alternative way to retrieve lters from the
library is to search for lters which are closest to the user choices in terms of
colour or texture. We call these the Colour Search (CS) and Texture Search (TS)
retrieval methods. We modied standard image retrieval techniques to perform
this search, using information derived oine about how the lters alter the colour
histogram and texture of some standard images, as described in further detail
in [11]. While the search techniques work with approximate information about
lters (to perform eciently), they are fairly accurate, i.e., in [11], we report a
93% probability of retrieving a given lter in a set of 10. Again, a record of the
library lters shown to the user is kept to avoid repetitions.
 Evolutionary methods. Representing image lters as trees enables both the
crossing over of branches into ospring from each of two parents, and the mu-
tation of trees. To perform crossover, we start with two parent trees (called the
top and bottom parent), and we choose a pair of nodes: Nt on the top parent
and Nb on the bottom parent. These are chosen randomly so that the size of the
tree above and including Nt added to the size of the tree gained from removing
the tree above and including Nb from the bottom parent is between a predened
minimum and maximum (3 and 15 for the experiments here). After 50 failed
attempts to choose such a pair of nodes, the two trees are deemed incompatible,
and a new couple is chosen. If they are compatible, however, the tree above and
including Nt is substituted for the tree above and including Nb to produce the
ospring. An example crossover operation is shown in gure 2a. We have imple-
mented various mutation techniques, which alter the lter by dierent amounts,
with details given in [3]. For the experiments here, we employ a mutation method
which randomly mutates a single transform/compositor node in the lter tree
240
Fig. 2. a) Example crossover operation. b) Filter Trouve design interface screenshot.
into a dierent transform/compositor node. This almost guarantees that a visi-
ble change will occur, and through experience we have found that it can produce
a range of changes to the lter, from mild to fairly strong, depending on where
the mutated node is in the tree. With the Cross-Over (CO) method, we choose
pairs of lters randomly from the set of chosen lters C and perform one-point
crossover to produce new lters for N. With the Mutate (MT) method, lters
from C are chosen randomly and mutated as above to produce lters for N.
Note that lters generated by the CO or MT methods are checked to determine
if they produce single colour images or the same image as another child of the
same parents. If so, the lter is rejected and another one is generated.
2.2 The Filter Trouve User Interface
The user interface employed for the experiments described below is portrayed
in gure 2b. In each session, the user works on a single design which involves
ltering a single image which is incorporated (possibly multiple times) into a
design, such as a magazine cover, etc. The user is shown the expression of 24
lters on the image in successive screens. They can click on a ltered image to
see it in the design (in gure 2b, a ltered version of a tree image is shown in
a magazine cover design). The user can also click a tick sign on each image to
express their preference for it. When a user has chosen the images they like from
a sheet, they click on the `next' button which supplies them with another sheet
of 24 images. At the end of a session, after clicking a `nalise' button, users are
shown all the designs that they ticked one by one in a random order. For each
design, they are asked to judge it by choosing one of: (1) would denitely not
use it (2) would probably not use it (3) not sure (4) would probably use it, and
(5) would denitely use it. These choices essentially provide a score between 1
and 5 for each design. When each design has been given a score, the session
ends. There has been considerable interest recently in creativity support tools
like Filter Trouve, which allow users to quickly generate, explore and compare
multiple alternatives [9]. Our design embraces Shneiderman's principle of \low
thresholds" for novices, but intentionally avoids the \high ceilings and wide
walls" of advanced and comprehensive functionality. Filter Trouve is designed
for the amateur creator [2], who is not motivated to gain domain expertise, as
opposed to the novice, who intends to become an expert. However, we see no
reason why Objet Trouve methods could not support other user groups.
241
3 Experiment 1
To recap, we are interested in comparing methods for presenting users with
successive sets of image lters, so that they can drive a browsing session via
their aesthetic choices. In this experiment, we investigate whether using tech-
niques which choose new lters based on the user's choices perform better than
techniques which choose them randomly. To do this, we compared the Random
From Category (RFC) generation technique with a hybrid technique which we
call Taster. This supplies the user with: 4 lters from MFC; 4 from CO; 4 from
MT; 3 from CS; 3 from TS and 6 from RFC. Note that we included lters re-
turned by RFC in the Taster method, as we found in initial tests that users
appreciate the variety provided by RFC. Note also that providing only 3 images
from the CS and TS methods was a mistake { while we had intended to provide
4 images for each of these methods, the mistake does not aect the conclusions
we draw. The two hypotheses we proposed were:
 The Taster method will produce better images than the RFC method, as
measured by the scores ascribed by participants to their designs.
 Taster will be quicker to use than RFC, as measured by the time to complete
tasks and number of images viewed before deciding they have nished the task.
We asked 29 participants with varying levels of graphic design experience (no
professionals) to undertake 4 design tasks using the Filter Trouve interface. The
design tasks were: a gallery installation, where a ltered image of a cityscape
was included four times with wooden frames; a magazine cover, which involved a
ltered image of a woman's face behind text; a Facebook prole, which involved
a ltered version of man's face; and a book cover, where a ltered version of a
(haunted) house appears on the front and back. We instructed participants to
tick any lters they liked on a sheet and to stop when either around 10 minutes
had passed, or they felt they had enough designs they were pleased with, or they
felt the search was futile and wanted to stop. We balanced the two experimental
conditions (i.e., the Taster method and the RFC method) in such a way that
each participant had both conditions twice. This meant that there were either 14
or 15 participants in each pairing of design task and condition. The measures for
each task were the time taken to complete the task, the number of sheets viewed
by the participant, the number of ticks and expansions (viewing the ltered
image in the overall design) a participant makes, and the score for each design.
3.1 Quality and Eciency Results
Some summary statistics about Taster and RFC are presented in table 1. The
data were analysed using SPSS v17.0. With all measures, it was noted that there
was substantial positive skew in many of the task conditions. For this reason,
non-parametric tests were used to compare the conditions. Additionally, as each
participant completes four separate tasks but not in a full-factorial design, the
measures for each task are considered separately as a between-subjects design
for the two conditions. However, to account for possible correlations between the
performance on the dierent tasks, we make a Bonferroni correction. Thus, for all
242
Condition Mean Score Mean Ticks Mean Expands Mean Time (s) Mean Sheets
RFC 3.23 17.59 43.07 497.21 9.48
Taster 3.47 15.97 39.53 463.55 6.97
Table 1. Mean score per chosen design; average number of ticks per design task; mean
number of expansions per design task; mean time per design task; mean number of
sheets of 24 images per design task, for both RFC and Taster conditions.
Design Task RFC Taster
Gallery 536 (251) 504 (123)
Magazine 409 (92.0) 473 (242)
Facebook 373 (179) 372 (128)
BookCover 673 (298) 508 (230)
Design Task RFC Taster
Gallery 6.87 (2.48) 4.71 (2.37)
Magazine 7.73 (2.69) 6.07 (2.76)
Facebook 12.1 (5.72) 10.5 (6.29)
BookCover 11.50 (5.20) 6.33 (2.82)
Table 2. a) mean (standard deviation) of task times in seconds for each task in each
condition. b) mean (sd) of number of sheets viewed for each task in each condition.
tests, we use the Mann-Whitney test with signicance level  = 0:05=4 = 0:0125,
and SPSS was used to produce exact p values. The mean and standard deviations
of the task times are given in table 2a. There are modest dierences between the
means, but overall there are no signicant dierences: for the gallery task, U =
102, p = 0:914; for the magazine task, U = 95:5, p = 0:691; for the Facebook task,
U = 104, p = 0:974; and for the book cover task, U = 57:5, p = 0:038. Note that
the book cover task is tending towards being completed signicantly quicker for
the Taster condition. For the number of lters viewed, in all tasks, participants
viewed fewer sheets in the Taster condition than in the RFC condition. The
means and standard deviations are shown in table 2b. Signicant dierences are
seen for Gallery (U = 49, p = 0:011) and for BookCover (U = 40, p = 0:003)
but not for Magazine (U = 63:5, p = 0:067) or Facebook (U = 86:5, p = 0:429).
Analysing scores is more complicated, as each participant was able to tick
and therefore score as many designs as they wished. The number of ticks depends
on personal strategies for using the system, hence it would be useful to see if
participants diered in the number of ticked designs in one condition over the
other. As tasks were fully counterbalanced across the two conditions, for each
participant, the number of scores produced was summed across tasks in each
condition. In the RFC condition, participants ticked a mean of 17.59 designs,
whereas in the Taster condition, the mean is lower, at 15.97. A Wilcoxon Signed
Ranks test indicates that these dierences are not signicant (Z = 􀀀1:14, p =
0:261). Taking instead the average (mean) of all scores for each participant over
two tasks in a single condition, the mean score in the RFC condition is 3.23
and in the Taster condition, it is higher, at 3.47. The dierence in mean scores
over the two conditions is signicant (Wilcoxon Z = 􀀀2:649, p = 0:007). It is
worth noting though that looking only at the number of score 5s, i.e., the images
people would denitely use, a similar analysis showed no signicant dierence.
In summary, users will on average be more satised (in terms of scores) with
designs produced by Taster. However, they will not necessarily tick more designs
nor give more designs the maximum score in a session. While users will not
necessarily nish quicker, they might be presented with fewer images in carrying
out a design task with Taster than RFC, but this is task dependent.
243
Fig. 3. a) Mean sheet scores for the RFC and the Non-RFC submethods in the Taster
method. b) tick and expand probabilities per sheet for RFC and Non-RFC submethods.
3.2 User Behaviour Analysis
As Taster is a combination of six lter selection submethods, we can look at the
individual contribution each submethod makes. Splitting the submethods into
those using the participant's choices (Non-RFC) and the RFC method, looking
at gure 3a, we see that for Non-RFC methods, the mean score for designs ticked
in sheets 1 to 5 is 3.23, whereas in sheets 6 to 10, the mean score is 3.4. The
same eect is not present in the sheets produced by the RFC method. This
suggests that users may appreciate the ability to drive the search with their tick
choices. Let us dene the probability of ticking, pt(n), for sheet number n as the
number of images ticked on the n-th sheet across all sessions divided by 24 (the
number of images shown on any sheet). Plotting this in gure 3b, we see that for
both RFC and Non-RFC methods, pt(n) consistently falls as n increases from
1 to 10. Dening the probability of expanding a design similarly as pe(n), we
see that this also decreases over sheets 1 to 10. This suggests that, in general,
participants became more discerning about the images they expanded and ticked
as they progressed through the rst 10 sheets. After sheet 10, the pattern is less
clear, perhaps due to the small number of sessions that lasted that long. Table
3 shows that ranking the methods by mean image score is equivalent to ranking
them by pt and (almost) by pe. This suggests a ranking by popularity, i.e., if
participants are ticking/expanding more designs during a session, they will give
a higher score on average to the designs they choose. We also note that the two
evolutionary techniques (CO and MT) perform the best in terms of the mean
score that users ascribe to the designs they produce.
To further analyse the dierence between the submethods, we investigated
the delity of the image lters. For a given lter f, we let D(f) denote the average
Euclidean RGB-distance between pairs of pixels in the original and ltered image
at the same co-ordinate, normalised by division by the maximum RGB-distance
possible. Note that we have experimented with measures based on the HSV
colour model, but we saw little dierence in the results. For a given design
session, S, we let D(S) denote the average of D(f) over all the lters f shown
to the user in the session. We let T(S) be the average of D(f) over all the lters
ticked by the user in session S. We also denote P(S) as the average Euclidean
RGB-distance between pairs of lters (f1; f2) in a session S, where f1 is a lter
244
Rank Method Mean Score pt pe Mean D(S) Mean T(S) Mean P(S)
1 CO 3.92 0.20 0.34 0.33 0.25 0.32
2 MT 3.73 0.17 0.33 0.35 0.27 0.33
3 CS 3.30 0.09 0.27 0.37 0.30 0.37
4 RFC 3.13 0.07 0.20 0.47 0.35 0.51
5 MFC 3.13 0.06 0.16 0.45 0.35 0.5
6 TS 3.00 0.04 0.19 0.47 0.37 0.51
Table 3. Taster submethods ranked by mean score; probability of being ticked (pt) or
expanded (pe); mean distance from original D(S); mean distance of ticked lters from
original T(S); mean distance from the ticked lters in the previous sheet P(S).
ticked by the user in sheet n and f2 is any of the 24 lters shown to the user in
sheet n+1. Table 3 shows D(S), T(S) and P(S) for the submethods used in the
Taster sessions. We see that the mean score increases as P(S) decreases, hence
participants seem to appreciate lters more if they are more similar to those
ticked in the previous sheet. Also, the mean score decreases as D(S) increases,
which suggests that participants may have preferred more conservative image
lters, i.e., which change the original image less. This is emphasised by the
fact that in all but 3 of the 116 design sessions, T(S) was less than D(S), i.e.,
participants ticked more conservative lters on average than those presented to
them in 97% of the design sessions. The extent of this conservative nature diers
by design task: Gallery: D(S) = 0:44, T(S) = 0:34; Magazine: D(S) = 0:44,
T(S) = 0:28; Facebook: D(S) = 0:46, T(S) = 0:30; BookCover: D(S) = 0:45,
T(S) = 0:35. Participants were particularly conservative with the Magazine
and Facebook tasks, as these require the ltering of faces, which was generally
disliked (as expressed through some qualitative feedback we recorded).
4 Experiment 2
To explore the observation that scores seem to be correlated with the delity of
the lters, we implemented further retrieval techniques which manage the overall
delity of lters presented to users. In particular, we implemented another hybrid
technique, Evolution (EVO), which returns 8 lters produced by CO, 8 lters
produced by MT and 8 lters produced by RFC. This choice was motivated
by the fact that CO and MT were appreciatively the best submethods from
experiment 1. We produced two variants of EVO to test against it. Firstly, the
EVO-S method replaces the 8 lters produced by RFC with lters chosen from
the library in such a way that the average D(f) value for the lters on each
sheet remains static at 0.25. This choice was motivated by 0.27 and 0.25 being
the mean of the D(f) values over the ticked lters produced by the CO and MT
submethods respectively. The EVO-D method is the second variant. In order to
supply lters for sheet number n, this method calculates the average, A, of D(f)
over the ticked lters on sheet n 􀀀 1. Then, EVO-D chooses 8 lters from the
library to replace those produced by the RFC submethod in EVO, in such a way
that they each have a D(f) value as close to A as possible.
The aim of the second experiment was to test the hypothesis that EVO-S,
EVO-D or both would be an improvement on the plain EVO method. A similar
245
Method D(f) Score Ticks Exps Time Sheets
EVO 0.28 3.45 18.9 30.6 347.1 6.3
EVO-S 0.25 3.32 22.2 32.3 392.0 6.7
EVO-D 0.27 3.15 20.1 27.8 362.2 6.4
Table 4. Statistics for exp. 2: Mean RGB distance
per design (delity); Mean score per chosen
design; mean ticks per design task; mean
expansions per design task; mean time (s) per
design task; mean sheets viewed per design task.
experimental setup as before was
employed, involving 24 partici-
pants, asked to undertake 6 new
design tasks, namely: more sta-
tionery; another gallery; another
magazine cover, shown in gure 3;
a poster; a calendar; and a menu
(note that we used no faces in the
designs, to avoid any biasing as in
the rst experiment). The EVO,
EVO-S and EVO-D methods were balanced around the six design tasks evenly,
so that each participant was given each method twice. The results are shown
in table 4. A statistical analysis revealed that the hypotheses that EVO-S or
EVO-D is better (in terms of eciency and mean score) are not supported by the
data. In fact, EVO has a higher mean score and lower mean time than both the
other methods. We speculate that EVO-S and EVO-D's balancing of the average
image delity results in many very high delity lters (i.e. very low RGB dis-
tance) being introduced to achieve the balance, and that in general these lters
are less satisfying to the user than the random selection used by EVO.
5 Conclusions and Further Work
To the best of our knowledge, there has been little study of user behaviour
with browsing systems for creative tasks such as evolutionary art [7]. A notable
exception is [5], where user interaction with the NEvAr evolutionary art tool
is described. We introduce the phrase Objet Trouve browsing to acknowledge
the push and pull between software leading the user and the user leading the
software in such systems. This raises the question of whether software could learn
from the user, or more ambitiously: take a creative lead in design projects. Such
behaviour might be appreciated by novice or amateur designers perhaps lacking
inspiration. We are taking deliberately small steps towards such creative systems
with experiments involving amateur designers to understand the nature of both
the dierent methods and user behaviour with respect to those methods. In
particular, we started with the straightforward hypothesis that using intelligent
techniques to deliver new image lters based on those chosen by the user would
be an improvement over supplying the lters randomly. (The truth of this is
rather taken for granted in evolutionary art and image retrieval systems).
Working with image lters enables us to compare and contrast methods from
dierent areas of computing: database, image retrieval and evolutionary methods
in browsing for resources (lters) in design tasks. In experiment 1, we found
that more intelligent methods will lead to greater satisfaction in the designs
produced and may lead to the completion of the design task with less eort (i.e.,
having to consider fewer possibilities). We also observed some user behaviours
such as becoming more discerning as a session progresses and appreciating the
progression aorded by the intelligent techniques. Furthermore, while there is
some correlation between lter delity and user satisfaction, we were unable
246
to harness this for improved browsing techniques, as shown in experiment 2.
Simply giving users more of what they like, whether statically or dynamically is
not sophisticated enough, raising interesting questions about managing novelty.
We plan to study other browsing systems, e.g., [10], which employs emotional
responses to web pages, and other evolutionary image ltering systems, e.g., that
of Neufeld, Ross and Ralph (chapter 16 of [7]), which uses a tness function based
on a Bell curve model of aesthetics. Moreover, to improve our experiments, we
will study areas of computer supported design such as: the in
uences of re
ec-
tion and emergence [8]; the use of analogy and mutation [4]; and how serendipity
can be managed [1]. We will test dierent browsing mechanisms involving dif-
ferent image analysis techniques such as edge information and moments, and
measures based on novelty, such as those prescribed in [6]. Despite the failure of
the EVO-D method in experiment 2, we believe that software which dynamically
employs information about a user's behaviour to intelligently suggest new arte-
facts can improve upon less sophisticated methods. In particular, we intend to
use the data from experiments 1 and 2 to see whether various machine learning
techniques can make sensible predictions about user preferences during image
ltering sessions. Our ultimate goal is to build and investigate software which
acts as a creative collaborator, with its own aesthetic preferences and goals, able
to work in partnership with both amateur and expert designers.
Acknowledgments
We would like to thank the anonymous reviewers for their valuable input. This
work is supported by EPSRC grants EP/F067127 and TS/G002835.
<references_biblio/>
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