Visual Saliency: the signal from V1 to capture attention

1
Visual Saliency the signal from V1 to capture
attention
Li Zhaoping Head, Laboratory of natural
intelligence Department of Psychology University
College London www.gatsby.ucl.ac.uk/zhaoping Dec
ember 2002
2
Visual tasks object recognition and
localization Pre-condition object
segmentation. Question how can one segment
object before recognition? (how can there be egg
before chicken?)

• I recently proposed (Li 1998, 1999, 2000, in
  particular Li TICS, Jan. 2002)
• Pre-attentive segmentation by highlighting
  conspicuous image areas where homogeneity breaks
  down (these areas are candidate locations for
  object boundaries, thus serving segmentation).
• V1 produces a saliency map containing such
  highlights, by intracortical interactions.

3
V1 produces a saliency map
The V1 model is based on V1 physiology and
anatomy (e.g., horizontal connections linking
cells tuned to similar orientations), tested to
be consistent with physiological data on
contextual influences (e.g., iso-orientation
suppression, Knierim and van Essen (1992)
co-linear facilitation, Kapadia et al 1995).
4
Original input
V1 response S
Saliency of an item is assumed to increase with
its evoked V1 response. We assume that efficiency
of a visual search task increases with the
salience of the target (or its most salient part,
e.g., the horizontal bar in the target cross
above). The high z score, z 7, (of the
horizontal bar), a measure of the cross
salience, enables the cross to pop out, since its
evoked V1 response (to the horizontal bar) is
much higher than the average population response
of the whole image. The cross has a unique
feature, the horizontal bar, which evokes the
highest response since it experiences no
iso-orientation suppression while all distractors
do. Hence, intra-cortical interaction is a neural
basis for why feature searches are often
efficient.
5
Signal saliency regardless of object features
contrary to common belief, cells signaling
saliency can also be tuned to features. V1 can
produce a saliency map even though its cells are
tuned to features. V1 neurons firing rate is the
currency for saliency, just like a US dollar is a
functional currency even if the holder has
his/her own particular nationality Chinese, US,
or other.
6
The V1 saliency map agrees with visual search
behavior.
7
The saliency map also explains spatial and
distractor effects.
8
(No Transcript)
9
Potential interactions with other team members of
the collaboration (self-centered)
Visual physiology, Cheng Kang
Visual attention, He Sheng
Feature binding, Chen Lin
Feature coding
Neural mechanisms
Top-down and bottom up interactions
Pre-attentive segmentation, V1 saliency map, Li
Zhaoping
Artificial vision, Zhang Jiajie
engineering
dynamics and representation
Mathematical modeling Zhang Jun
learning vs. attention
Perceptual learning, Lu Zhonglin