Object-based Visual Attention: a Model for a Behaving Robot
Francesco Orabona, Giorgio Metta and Giulio Sandini
LIRA-Lab, DIST
University of Genoa, Genoa, ITALY 16145{bremen,pasa,giulio}@liralab.it

Abstract
One of the first steps of any visual system is that oflocating suitable interest points, “salient regions”, inthe scene, to detect events, and eventually to direct
gaze toward these locations. In the last few years,
object-based visual attention models have received anincreasing interest in the literature, the problem, inthis case, being that of creating a model of“objecthood” that eventually guides a saliency
mechanism. We propose here an object-based model ofvisual attention and show its instantiation on ahumanoid robot. The robot employs action to learn
and define its own concept of objecthood.

1. Introduction
Humans and many animals have a space-variant visualsystem that require them to move their eyes, threetimes a second on average, in order to position their
foveae onto interesting locations of the visual space.
This allows taking a series of small “snapshots” at very
high-resolution. The fact that this is the only way thatallows clear “vision” implies the existence of an
attention system which, at any moment in time, selects
the point to fixate next.
This leads to two sorts of questions: i) how to movethe eyes efficiently to important locations in the visual
scene, and ii) how to decide what is important and, as aconsequence, where to look next. To answer thesequestions, we should note first that the human visualsystem extracts basic information from the retinal
image in terms of lines, edges, local orientation etc.
Vision though does not only represent visual features
but also the things that such features characterize. In
order to segment a scene in items, objects, that is togroup parts of the visual field as coherent wholes, theconcept of “object” must be known to the system. In
particular, there is an intriguing discussion underway
in vision science about reference to entities that havecome to be known as "proto-objects" or "pre-attentive
objects" [1]. These are a step above the mere localizedfeatures, possessing some but not all of thecharacteristics of objects.
The visual attention model we propose considers
these first stages of the human visual processing, andemploys a concept of salience based on “protoobjects” defined as blobs of uniform color in theimage. Since we are considering an embodied system
we will use the output of an instantiation of the model
to control the fixation point of a robotic head.
Moreover, through action, the attention system can go
beyond proto-objects [2]. In fact, once an object isgrasped, the robot can move and rotate it to build astatistical model of the features belonging to it,
constructing a representation as a collection of protoobjects and their relative spatial locations. This internal
representation then generates a top-down signal thatbias attention toward known objects; as an example wewill show how the top-down influence can be used todirect the attention of the robot to spot a specific object
among other similar items lying on a table.
The rest of the paper is organized as follows.
Section 2 contains an introduction on the modeling of
human visual attention. Section 3 describes theexperimental setup used in the experiments. Section 4
details the robot’s visual system and theimplementation. Section 5 introduces the probabilistic
object model and shows how this is used for object
recognition. Finally in sections 6 and 7 we show
experimental results and we draw some conclusions.

2. Human visual attention

3. Setup

The attempts of modeling human visual attention
traditionally rest on two assumptions. On the one hand,the space-based theory holds that attention is allocatedto a region of space, with processing carried out only
within a certain spatial window. This theory considers
attention as a “spotlight”, an internal eye or a sort of“zoom lens”; attention is deployed as a spatial
gradient, centered on a particular location.
On the other hand, object-based attention theories
argue that attention is directed to an object or a groupof objects, to process specific properties of the selected
objects, rather than regions of space. There is a
growing evidence both from behavioral and fromneurophysiological studies that shows, in fact, that
selective attention frequently operates on an objectbased representational medium in which theboundaries of segmented objects, and not just spatial
position, determine what is selected and how attention
is deployed (see [3] for a review). This reflects the factthat the visual system is optimized for segmenting
complex scenes into representations of (often partly
occluded) objects to be used both for recognition andaction, since perceivers must interact with objects andnot with disembodied spatial locations.
Finally, another classification can be madedepending on which cues are actually used inmodulating attention. Bottom-up information, whichcomes only from the input image, includes basic
features such as color, orientation, motion, depth, andtheir conjunction thereof. A feature or a stimulus
catches attention if it differs from its immediatesurrounding in some dimensions and the surround isreasonably homogeneous in those same dimensions.
However, in attention, higher-level mechanisms are
involved as well. A bottom-up stimulus, for example,
may be ignored if attention is already focused
elsewhere [4]. In this case attention is also influenced
by top-down information relevant to the particular task
at hand which is not necessarily available in the image.
In the literature a number of attention models that
follow the first hypothesis have been proposed [5-7].
Most of them are derived from Treisman’s Feature
Integration Theory (FIT) [8], which employs a separate
set of low-level feature maps that are combined
together by a spatial attention window operating on amaster saliency map. An important alternative model isgiven by Sun and Fisher [9], which propose an
combination of object-and feature-based theories.
This paper presents an object-based model of visual
attention that integrates bottom-up and top-down cues;in particular, top-down information works as a priming
mechanism for certain regions in the visual search task.

The experiments reported in the paper were carried out
on a robotic platform called Babybot. This is ahumanoid upper torso which consists of a head, an arm
and a hand. The head has 5 degrees of freedom, two of
which control the neck pan and tilt, whereas the otherthree actuate two eyes to pan independently and tilt on
a common axis. The arm is the well known Unimate
PUMA 260, an industrial manipulator with 6 degrees
of freedom; the hand has 5 fingers for a total of 6
degrees of freedom.
From the point of view of the sensors, the head isequipped with two cameras and two microphones forvisual and auditory feedback. Proprioceptive
information is provided to the robot by optic andmagnetic encoders mounted on all joints of the head,arm and hand. More details about the Babybot can befound for example in [10].

4. ModelA block diagram of the model is shown in Figure 1;
the input is a sequence of color log-polar images [11].
The use of log-polar images comes from theobservation that the distribution of the cones, i.e. thephotoreceptors of the retina involved in diurnal vision,
is not uniform. This distribution seems to influence thescanpaths during a visual search task and so it has tobe taken into account to better model overt visual
attention [12]. In addition, the lower resolution of theperiphery of the field of view reduces the images’ size
and thus reduces the computational load.
Log-Polar
color image

+
R+G-

G+R-

B+Y-

Edge detection

Combinededges
SmoothingColor
quantization

Watershed
Saliencycomputation

Top-Down
information

Figure 1. Block diagram of the model.

Figure 2. Log-polar transform of an image.

4.1. Log-polar images

has a center-surround receptive field (RF) with
spectrally opponent color responses. That is, forexample, a red input in the center of a particular RF
increases the response of the channel R + G − , while agreen one in the surrounding will decrease its
response. The spatial response profile of the RF isexpressed by a Difference-of-Gaussians (DoG) over
the two sub-regions of the RF, ‘center’ and ‘surround’.
A response is computed as there was a RF centered oneach pixel of the input image, thus generating an
output image of the same size of the input. This
operation, considering for example the R + G − channel
is expressed by:
R + G − ( x, y ) = α ⋅ R ∗ g c − β ⋅ G ∗ g s(3)
The two gaussian functions, g c and g s , are notbalanced: the ratio β / α is chosen equal to 1.5,
consistent with the study of Smirnakis et al. [13]. The
unbalanced ratio preserves the achromatic information:that is, the response of the channels to a uniform grayarea is not zero. Hence the model does not need toprocess achromatic information explicitly since it is
implicitly encoded, similarly to what happens in thehuman retina’s P-cells [14]. The ratio σs /σc, thestandard deviation of the two gaussian functions, ischosen equal to 3. To be noted that by filtering a logpolar image with a standard space-invariant filter leads
to a space-variant filtered image of the original
cartesian image [15].
Edges are then extracted on the three channels
separately using a generalization of the Sobel filter due
to [16], obtaining ERG(x,y), EGR(x,y) and EBY(x,y). A
single edge map is generated combining the tree
outputs:

The log-polar mapping is a model of the topologicaltransformation of the primate visual pathways from theretina to the visual cortex. Cones have a higher density
in the central region called fovea (approximately 2° of
the visual field), while they are sparser in the
periphery. Consequently, the resolution is higher anduniform in the center while it decreases in the
periphery, moving away from the fovea.
From the mathematical point of view the log-polar
mapping can be expressed as a transformation between
the polar plane (ρ,θ) (retinal plane), the log–polar
plane (ξ,η) (cortical plane) and the Cartesian plane(x,y) (image plane), as follows [11]:⎧ η = q ⋅θ
⎪
(1)
ρ
⎨
⎪ξ = log a ρ0⎩where ρ0 is the radius of the innermost circle, 1/q is theminimum angular resolution of the log-polar layoutand (ρ,θ) are the polar co-ordinates. These are related
to the conventional Cartesian reference system by:⎧ x = ρ ⋅ cos θ(2)⎨⎩ y = ρ ⋅ sin θ
Figure 2 shows a Cartesian image and its log-polar
counterpart as derived from Equations (1) and (2).

The log-polar transform has the side effect of
sharpening the edges near the fovea due to themagnification factor of the mapping; this iscompensated multiplying each pixel by a factor whichis exponential on the eccentricity.

4.2. Feature extraction

4.3. Proto-objects

As a first step the input image at time t is averaged
with the output of a color quantization procedure (seelater) applied to the image at time t-1. This is to reduce
the effect of the input noise. The red, green, blue
channels of each image are then separated, and theyellow channel is constructed as the arithmetic meanof the red and green channels. Successively these fourchannels are combined to generate three color
opponent channels, similar to those of the retina. Eachchannel, normally indicated as R + G − , G + R − , B +Y − ,It has been speculated, that synchronizations of visual
cortical neurons might serve as the carrier for theobserved perceptual grouping phenomenon [17, 18].
The differences in the phase of oscillation among
spatially neighboring cells is believed to contribute to
the segmentation of different objects in the scene.
We have used a watershed transform (rainfallingvariant) [19] on the edge map to simulate the result of
this synchronization phenomenon and to generate theproto-objects.

{}

E ( x, y ) = max ERG ( x, y ) , EGR ( x, y ) , EBY ( x, y ) (4)Input image

+

R+G-

G+R-

B+YEdges

Color quantization

Salience Map

As already mentioned above, a feature or a stimulus
catches the attention of the system if it differs from its
immediate surrounding. We chose to compute thebottom-up salience as the Euclidean distance in thecolor opponent space between each blob and its
surrounding. The size of the spot or focus of attention
is not constant: it changes depending on the size of theobjects in the scene. To account for this fact the greaterpart of the visual attention models in literature uses amulti-scale approach filtering with some type of “blob”
detector (typically a difference of Gaussian filter) at
various scales [21]. We reasoned that this approachlacks continuity in the choice of the size of the focus ofattention. We propose instead to dynamically vary theregion of interest depending on the size of the blobs.
That is the salience of each blob is calculated inrelation to a neighborhood proportional to its size. In
our implementation we consider a rectangular region 3times the size of the bounding box of the blob assurrounding region, centered on each blob. The choiceof a rectangular window is not incidental, rather it waschosen because filters over rectangular regions can becomputed efficiently by employing the integral image
as in [22]. The bottom-up saliency is thus computed as:
Sbottom − up = ∆RG 2 + ∆GR 2 + ∆BY 2∆RG = R + G − − R + G −Figure 3. Example of model maps.
The intuitive idea underlying this method comes fromgeography: a topographic relief is flooded by water,watershed are the divide lines of the domains of
attraction of rain falling over the region. In our view
the watershed transform simulates the parallel spreadof the activation on the image, until this procedure fills
all the spaces between edges. Differently from othersimilar methods the edges themselves will never betagged as blobs and the method does not requirecomplex membership functions either. Moreover the
result does not depend on the order in which the points
are examined like in standard region growing [20]. As
a result, the image is segmented into blobs with either
uniform or uniform gradient of color.
Each blob is tagged with the average of the color of
the pixels within its area (this leads to a sort of color
quantizated image). The result is blurred with agaussian filter and stored: this will be used to perform
a time-smoothing by simple averaging with the frame
at time t+1 to reduce the effect of noise and increase
the temporal stability of the blobs. After an initialstartup time of about five frames, the number of blobs
and their shape stabilize. If movement is detected in
the image (as difference between two consecutive
frames) then the smoothing procedure is halted and thebottom-up saliency map becomes the motion image.

blob+surround−∆GR = G R − G + R −
blob

(5)

surround∆BY = B +Y − − B +Y −
blob

where

surroundindicates the average of the image values

over a certain area (indicated in the subscripts).
The top-down influence on attention is, at the
moment, calculated in relation to the task of visually
searching a given object. In this situation a model ofthe object to search in the scene is given (see Section5) and this information is used to bias the saliency
computation procedure. In practice, the top-downsaliency map is computed as the Euclidean distance inthe color opponent space, between each blob’s averagecolor and the average color of the target:
Stop − down = ∆RG 2 + ∆GR 2 + ∆BY 2∆RG = R + G − − R + G −blob+

object−∆GR = G R − G + R −blob+∆BY = B Y
blob

object−− B +Y −objectwith a notation similar to the one above.

(6)

Blobs that are too small (1/550 of image area) or
too big (1/4 of the image area) are discarded from thecomputation of salience and will not be considered as
possible candidates to be part of objects. The blob in
the center of the image (currently fixated) is ignored
also because it cannot be the target of the next fixation.
The total salience is simply calculated as the linear
combination of the top-down and bottom-up
contributions:S = ktd ⋅ Stop − down + kbu ⋅ Sbottom − up(7)
and normalized in the range 0-255. The center ofmass of the most salient blob is selected for the next
saccade. An example of the intermediate and final
maps of bottom-up salience is shown in Figure 3. All
the computations are done on log-polar images, but
input and output images are shown remapped tocartesian for clarity.

4.4. Inhibition of return
In order to avoid being redirected immediately to apreviously attended location, a local inhibition istransiently activated in the saliency map. This is called“inhibition of return” (IOR) and it has been
demonstrated in human visual psychophysics. IOR
does not seem to function in retinal coordinates but it
is instead attached to environmental locations. It has
been proposed that the IOR is required to allow anefficient visual search by discouraging shifting theattention toward locations that have already beeninspected, and it seems to be working also in the caseof moving objects (for a review see [23]).
All these findings lead to the conclusion that thehuman visual system works by tagging objects andmoving tags as objects move, hence the IOR seems to
be coded in an object-based frame of reference.
Our system implements a simple object-based IOR.
A list of the last five positions visited [24] ismaintained in a head-centered coordinate system andupdated with a FIFO (First In First Out) policy. The
position of the tagged blob is stored together with theinformation about its color. When the robot gazemoves – for example by moving the eyes and/or thehead – the system keeps track of the blobs it has
visited. These locations are inhibited only if they show
the same color seen earlier: so in case an inhibited
object moves or its color changes, the location
becomes available for fixation again.

5. Learning about objects
We assume the robot has already grasped the object;
this can happen because a collaborative human has
given the object to the robot or because it has

autonomously grasped the object (even by chance
initially). Both solutions are valid bootstrapping
behaviors for the acquisition of an internal model ofthe object. When the robot holds the object it can
explore it by moving and rotating it.
Objects are represented by the blobs generated by
the visual attention system and their relative positions(neighboring relations). The model is createdstatistically by looking at the same object for sometime from different points of view. A histogram of thenumber of times a particular blob is seen is used toestimate the probability that the blob belongs to thegrasped object.
In the following, we use the probabilistic
framework proposed by Schiele and Crowley [25]. We
want to calculate the probability of the object O givena certain local measurement M. This probability
P(O|M) can be calculated using Bayes’ formula:P ( M | O ) P (O )(8)
P (O | M ) =P(M )where: P(O) the a priori probability of the object O,
P(M) the a priori probability of the local measurement
M, and P(M|O) is the probability of the local
measurement M when the object O is fixated. In thefollowing experiments we only carried out a detection
experiment for a single object, there are consequently
only two classes, one representing the object andanother representing the background. P(O) and P(~O)
are simply set to 0.5 because they do not affect theorder of the maxima of P(O|M). Since a single blob is
not discriminative enough, we considered theprobabilities of observing pairs of blobs instead. To
simplify the probability estimation (the number ofpossible combinations) we have chosen to observe
only pairs composed of the central blob (taken as
reference) and one surrounding blob as the local
measurement M:
P ( M | O ) = P ( Bi | Bc and ( Bi adiacent Bc ) ) (9)where Bi is the i-th blob that surrounds the central blob
Bc that belongs to the object O. That is, we exploit thefact the robot is fixating the object and assume thecentral blob will be constant across fixations. The
color of the central blob will be stored and used to bias
the visual search (see Section 4.3). The probabilities
P(M|~O) are estimated during the exploration phase byconsidering the blobs not adjacent to the central blob.
The local measurements are considered independent
because they refer to different blobs, so we factorize
the total probability P(M1,…,MN|O) in the product ofthe probabilities P(Mi|O). An object is considered‘found’ if the probability P(O|M1,…,MN) is greaterthan a fixed threshold. When the object is found after
visual search, a figure-ground segmentation isattempted: each blob is selected if it is adjacent to thecentral recognized blob and if its probability to belong
to the object is greater of 0.5.
In practice, we estimate the probability of all blobs
adjacent to the central blob to belong to the object.
This procedure, although requiring the “active
participation” of the robot (through gazing) is fasterthan estimating all probabilities for all possible pairs ofblobs of the fixated object. Estimation of the full joint
probabilities would require a larger training set thanthe one we were able to use in our experiments. Our
experimental scenario required the construction of theobject model on the fly with the shortest possible
exploration procedure, which naturally leads toestimating probabilities with few samples. It is likelythat many bins in the histograms, used to estimate
probabilities, are empty. To overcome this problem wehave used a probability smoothing method. In
particular we employed as zero count smoothing the
Lidstone’s law of succession:count ( M ∧ O) + λ
P ( M | O) =(10)
count (O) + vλfor a v valued problem. With λ=1 and a two valued
problem (v=2), we obtain the well-known Laplace’s
law of succession. Following the results of Kohavi etal. [26], we choose λ=1/n where n is equal to thenumber of images utilized during the training phase.
A first use of the system is to create a visual modelof the hand of the robot (a special object). By relying
on this model the robot can distinguish the grasped
object from parts of the hand that might still be visible.

6. Results
The behavior of the robot during the learning phases isshown in Figure 5: all the blobs bordering the central
one (blue) are used for learning the visual appearanceof the object.
Two examples of the saliency map are shown in
Figure 4: in 4.4 there is a purely bottom-up (ktd=0,
kbu=1 in Equation (7)) map which is the result of theprocessing of the scene in 4.1; in 4.5 there is a purely
top-down (ktd=1, kbu=0) map output after theprocessing of 4.2. In the latter the robot was instructed
to search for the toy airplane. After a saccade on theobject and a successfully recognition the figure-groundsegmentation is shown in Figure 4.6. The center ofmass of the segmented object is used to guide thegrasping action of the robot.
We have tested the attention system while guiding
the recognition and grasping of objects in the Babybot.
In order to qualitatively evaluate the performance, wehave done a comparison test of the bottom-up attention

using the database of images by Itti et al. [27] (color
images with an emergency triangle and relative binary
segmentation masks of the triangle), which is freely
available on the Internet (http://ilab.usc.edu/imgdbs/).
First, the original images and segmentation masks arecropped to a square and transformed to the log-polar
format (252x152 pixels) (see Figure 6.1 and Figure 6.2
for the cartesian remapped images). To simulate thepresence of a static camera, the images are presented tothe system continuously and, after five “virtual”
frames, the bottom-up saliency map is confronted withthe mask. In 49% of the images a point inside theemergency triangle was selected as the most salient(see an example in Figure 6.3). It is worth noting that a
direct comparison with the results of Itti et al., bycounting the number of false detection before thetarget object is found, was not possible since after eachsaccade the log-polar image should change completely.

Figure 4. Example saliency maps. In (4) there is thebottom-up saliency map of the image (1). In (5) the topdown saliency map of (2), while searching for the blue
toy airplane. Image (6) is the figure-groundsegmentation of the image in (3), after having
recognized the object.

Figure 5. Some example images during exploration
phase (1-3) and related segmentations (4-6) used tobuild the statistical model of the object.

Acknowledgments

Figure 6. Result on a static example image.

The research described here has been funded by the
project MIRROR (IST-2000-28159), ADAPT (IST2001-371173), and ROBOTCUB (IST-004370).
The authors wish to thank the anonymous reviewers
for the fruitful comments and discussion on the earlier
version of this manuscript.

7. Conclusion

References

We have presented the implementation of a visual
attention system employing both top-down andbottom-up information. It runs in real time on astandard Pentium class processor and it is used tocontrol the overt attention mechanism of a humanoid
robot. This eventually gives rise to a different sort ofproblems
compared
to
the
more
typicalimplementations that only generate scan paths on static
images.
The algorithm divides the visual scene in color
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A similar approach has been taken by Sun andFisher [9] but the main difference with this work is thatthey have assumed that a hierarchical set of perceptualgroupings is provided to the attention system by someother means and considered only covert attention.
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selecting objects to be grasped, by helping the visual
search and recognition task.
As a possible extension, the watershed transform
could be extended to additional dimensions in feature
space (e.g. local orientation) thus providing new waysof both segmenting and recognizing objects.

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