An oscillatory correlation model of object-based attention

Attention is a critical mechanism for visual scene analysis. By means of attention, it is possible to break down the analysis of a complex scene to the analysis of its parts through a selection process. Empirical studies demonstrate that attentional selection is conducted on visual objects as a whol...

Full description

Saved in:
Bibliographic Details
Published in2009 International Joint Conference on Neural Networks pp. 2596 - 2602
Main Authors Quiles, M.G., DeLiang Wang, Liang Zhao, Romero, R.A.F., De-Shuang Huang
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.06.2009
Subjects
Computer science
Frequency synchronization
Image analysis
Image segmentation
Intelligent networks
Layout
Machine intelligence
Neural networks
Neurons
Oscillators
Online AccessGet full text

Cover

More Information
Summary:Attention is a critical mechanism for visual scene analysis. By means of attention, it is possible to break down the analysis of a complex scene to the analysis of its parts through a selection process. Empirical studies demonstrate that attentional selection is conducted on visual objects as a whole. We present a neurocomputational model of object-based selection in the framework of oscillatory correlation. By segmenting an input scene and integrating the segments with their conspicuity obtained from a saliency map, the model selects salient objects rather than salient locations. The proposed system is composed of three modules: a saliency map providing saliency values of image locations, image segmentation for breaking the input scene into a set of objects, and object selection which allows one of the objects of the scene to be selected at a time. This object selection system has been applied to real images and the simulation results show its effectiveness.
ISBN:142443548X
9781424435487
ISSN:2161-4393
2161-4407
DOI:10.1109/IJCNN.2009.5178597