The Selective Attention for Identification Model (SAIM): Simulating Visual Search in Natural Colour Images

Authors:
Dietmar Heinke;Andreas Backhaus;Yarou Sun;Glyn W. Humphreys
Affiliations:
Behavioural and Brain Sciences Centre, University of Birmingham, Birmingham B15 2TT, United Kingdom;Behavioural and Brain Sciences Centre, University of Birmingham, Birmingham B15 2TT, United Kingdom;Behavioural and Brain Sciences Centre, University of Birmingham, Birmingham B15 2TT, United Kingdom;Behavioural and Brain Sciences Centre, University of Birmingham, Birmingham B15 2TT, United Kingdom
Venue:
Attention in Cognitive Systems. Theories and Systems from an Interdisciplinary Viewpoint
Year:
2008

Citing 6
Cited 0

A Model of Saliency-Based Visual Attention for Rapid Scene Analysis

IEEE Transactions on Pattern Analysis and Machine Intelligence
SAIM: A Model of Visual Attention and Neglect

ICANN '97 Proceedings of the 7th International Conference on Artificial Neural Networks
Attentional Selection for Object Recognition A Gentle Way

BMCV '02 Proceedings of the Second International Workshop on Biologically Motivated Computer Vision
Contextual Learning in the Selective Attention for Identification model (CL-SAIM): Modeling contextual cueing in visual search tasks

CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops - Volume 03
Selective Attention for Identification Model: Simulating visual neglect

Computer Vision and Image Understanding - Special issue: Attention and performance in computer vision
Modeling grouping through interactions between top-down and bottom-up processes: the grouping and selective attention for identification model (G-SAIM)

WAPCV'04 Proceedings of the Second international conference on Attention and Performance in Computational Vision

Quantified Score

Hi-index	0.00

Visualization

Abstract

We recently presented a computational model of object recognition and attention: the Selective Attention for Identification model (SAIM) [1,2,3,4,5,6,7]. SAIM was developed to model normal attention and attentional disorders by implementing translation-invariant object recognition in multiple object scenes. SAIM can simulate a wide range of experimental evidence on normal and disordered attention. In its earlier form, SAIM could only process black and white images. The present paper tackles this important shortcoming by extending SAIM with a biologically plausible feature extraction, using Gabor filters and coding colour information in HSV-colour space. With this extension SAIM proved able to select and recognize objects in natural multiple-object colour scenes. Moreover, this new version still mimicked human data on visual search tasks. These results stem from the competitive parallel interactions that characterize processing in SAIM.