Keywords

Visual attention, object recognition, biased competition, top-down, visual search

Abstract

Visual attention models can explain a rich set of physiological data (Reynolds & Heeger, 2009, Neuron), but can rarely link these findings to real-world tasks. Here, we would like to narrow this gap with a novel, physiologically grounded model of visual attention by demonstrating its objects recognition abilities in noisy scenes.

To base the model on physiological data, we used a recently developed microcircuit model of visual attention (Beuth & Hamker, in revision, Vision Res) which explains a large set of attention experiments, e.g. biased competition, modulation of contrast response functions, tuning curves, and surround suppression. Objects are represented by object-view specific neurons, learned via a trace learning approach (Antonelli et al., 2014, IEEE TAMD). A visual cortex model combines the microcircuit with neuroanatomical properties like top-down attentional processing, hierarchical-increasing receptive field sizes, and synaptic transmission delays. The visual cortex model is complemented by a model of the frontal eye field (Zirnsak et al., 2011, Eur J Neurosci).

We evaluated the model on a realistic object recognition task in which a given target has to be localized in a scene (guided visual search task), using 100 different target objects, 1000 scenes, and two backgrounds. The model achieves an accuracy of 92% at black, and of 71% at white-noise backgrounds. We found that two of the underlying, neuronal attention mechanisms are prominently relevant for guided visual search: amplification of neurons preferring the target; and suppression of neurons encoding distractors or background noise.

Start Date

13-5-2015 10:40 AM

End Date

13-5-2015 11:05 AM

Session Number

01

Session Title

Motion, Attention, and Eye Movements

 
May 13th, 10:40 AM May 13th, 11:05 AM

Object Recognition and Visual Search with a Physiologically Grounded Model of Visual Attention

Visual attention models can explain a rich set of physiological data (Reynolds & Heeger, 2009, Neuron), but can rarely link these findings to real-world tasks. Here, we would like to narrow this gap with a novel, physiologically grounded model of visual attention by demonstrating its objects recognition abilities in noisy scenes.

To base the model on physiological data, we used a recently developed microcircuit model of visual attention (Beuth & Hamker, in revision, Vision Res) which explains a large set of attention experiments, e.g. biased competition, modulation of contrast response functions, tuning curves, and surround suppression. Objects are represented by object-view specific neurons, learned via a trace learning approach (Antonelli et al., 2014, IEEE TAMD). A visual cortex model combines the microcircuit with neuroanatomical properties like top-down attentional processing, hierarchical-increasing receptive field sizes, and synaptic transmission delays. The visual cortex model is complemented by a model of the frontal eye field (Zirnsak et al., 2011, Eur J Neurosci).

We evaluated the model on a realistic object recognition task in which a given target has to be localized in a scene (guided visual search task), using 100 different target objects, 1000 scenes, and two backgrounds. The model achieves an accuracy of 92% at black, and of 71% at white-noise backgrounds. We found that two of the underlying, neuronal attention mechanisms are prominently relevant for guided visual search: amplification of neurons preferring the target; and suppression of neurons encoding distractors or background noise.