Visual Attention: Images
We present in this page a few results obtained with the computer simulation
implementing our model of bottom-up, saliency-based visual attention.
The trajectory followed by the focus of attention is visualised by a red
broken line, with arrows indicating the sequential order in which the
various locations are attended. The focus of attention is represented by
a yellow circle. Because the saliency map is encoded at a coarse spatial
scale, the exact location of the center of the focus of attention may
appear slightly off an object of interest, although it is actually the object
that has been detected.
This model is under permanent development. This means that new
computational strategies are experimented almost every day. These
images may consequently exhibit attentional trajectories slightly
different from what you might have previously seen created with an
earlier version of the model. All the images in this page were created
with the same version of our model (May 2, 1996). No tuning was
- Images similar to these ones were used to
test for expected behaviors of
the model. For example, objects of similar shape but with different
contrast or color to the background are sequentially attended in order of
decreasing contrast or color, which in these cases are the only cues
- A noisy version of the classical 'pop-out' and
'feature search' psychophysical experiments proposed by A. Triesman
was simulated with our model. When a target object (here a bar) can be
distinguished from some distractors (different bars) by one or more unique
attributes (e.g. color or orientation), the measured time required by
humans to find the target is nearly independent of the number of
distractors in the image. This was verified by our model. However,
when the target can only be distinguished from the distractors by a
conjunction of attributes (e.g. in the right image below, the target
is the only bar which is at the same time red and oriented like the
green bars), the time required by humans to find the target increases
linearly with the number of distractors. This result was also verified
by our model.
Gazeous Beverage Can
- A first set of natural scenes studied consists of
finding a red can in environments exhibiting strong and distracting
contrasts in luminance, chrominance, and orientation.
German Traffic Signs
- Since traffic signs have been designed to be salient
and attract the attention of vehicle drivers, we decided to evaluate
the performance of our model in detecting them as salient objects in
natural scenes. This image database was kindly provided by
We experimented how the addition of strong speckle noise could
influence the performance of the model. In these examples, an important
amount of noise could be added before observing a degradation in
performance, as long as this noise did not directly interact with the
target (e.g. here, in the red-green channel). When the noise was interfering
directly with the target, however, the saliency of the target was decreased
(e.g. here, when numerous locations in the image become red, the red target
is no more particularly conspicuous in the image).
Performance of the model is difficult to quantitatively evaluate
with natural images. However, we obtained in general good agreement
between our personal perception of the salient locations in an image
and the attentional trajectory generated by the model.
Copyright © 1996 Laurent Itti (E-mail: firstname.lastname@example.org).
This site is not guaranteed bug-free ! ..........