Paper Info
Title
Top–Down Gaze Movement Control in Target Search Using Population Cell Coding of Visual Context
Abstract
Visual context plays an important role in humans' top-down gaze movement control for target searching. Exploring the mental development mechanism in terms of incremental visual context encoding by population cells is an interesting issue. This paper presents a biologically inspired computational model. The visual contextual cues were used in this model for top-down eye-motion control on searching targets in images. We proposed a population cell coding mechanism for visual context encoding and decoding. The model was implemented in a neural network system. A developmental learning mechanism was simulated in this system by dynamically generating new coding neurons to incrementally encode visual context during training. The encoded context was decoded with population neurons in a top-down mode. This allowed the model to control the gaze motion to the centers of the targets. The model was developed with pursuing low encoding quantity and high target locating accuracy. Its performance has been evaluated by a set of experiments to search different facial objects in a human face image set. Theoretical analysis and experimental results show that the proposed visual context encoding algorithm without weight updating is fast, efficient and stable, and the population-cell coding generally performs better than single-cell coding and k-nearest-neighbor (k-NN)-based coding.
Year
DOI
Venue
2010
10.1109/TAMD.2010.2053365
IEEE T. Autonomous Mental Development
Keywords
Field
DocType
eye,proposed visual context,population cell coding mechanism,population cell,cellular biophysics,image coding,encode visual context,motion control,neurophysiology,incremental visual context,visual contextual cue,population cell coding,visual context,learning (artificial intelligence),visual context decoding,incremental visual context encoding,gaze movement control,top-down eye-motion control,mental development mechanism,encoded context,neural network system,k-nearest-neighbor based coding,target search,object detection,biologically inspired computational model,top-down gaze movement control,computational model,neurons,visual context encoding,target searching,neural nets,neural encoding and decoding,medical image processing,new coding neuron,developmental learning mechanism,image motion analysis,computer model,k nearest neighbor,learning artificial intelligence,neural network,encoding,visualization,feature extraction,code generation,top down,decoding
Population,ENCODE,Gaze,Computer science,Visualization,Coding (social sciences),Feature extraction,Artificial intelligence,Artificial neural network,Machine learning,Encoding (memory)
Journal
Volume
Issue
ISSN
2
3
1943-0604
Citations 
PageRank 
References 
4
0.46
23
Authors
5
Name
Order
Citations
PageRank
Jun Miao122022.17
Laiyun Qing233724.66
Baixian Zou361.88
Lijuan Duan421526.13
Wen Gao511374741.77