文献类型：期刊文献

英文题名：Top-Down Gaze Movement Control in Target Search Using Population Cell Coding of Visual Context

作者：Miao, Jun[1];Qing, Laiyun[2];Zou, Baixian[3];Duan, Lijuan[4];Gao, Wen[5]

第一作者：Miao, Jun

通讯作者：Miao, J[1]

机构：[1]Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, Beijing 100190, Peoples R China;[2]Chinese Acad Sci, Sch Informat Sci & Engn, Grad Univ, Beijing 100049, Peoples R China;[3]Beijing Union Univ, Dept Informat Sci & Technol, Coll Arts & Sci, Beijing 100083, Peoples R China;[4]Beijing Univ Technol, Coll Comp Sci & Technol, Beijing 100124, Peoples R China;[5]Peking Univ, Inst Digital Media, Beijing 100871, Peoples R China

第一机构：Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, Beijing 100190, Peoples R China

通讯机构：[1]corresponding author), Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, Beijing 100190, Peoples R China.

年份：2010

卷号：2

期号：3

起止页码：196-215

外文期刊名：IEEE TRANSACTIONS ON AUTONOMOUS MENTAL DEVELOPMENT

收录：;EI(收录号：20103813242044);Scopus(收录号：2-s2.0-77956594467);WOS:【SCI-EXPANDED(收录号:WOS:000294902200003)】；

基金：This research was supported in part by the National Basic Research Program of China (2009CB320902), the Natural Science Foundation of China (60673091, 60702031, and 60970087), the Hi-Tech Research and Development Program of China (2006AA01Z122), the Beijing Natural Science Foundation (4072023 and 4102013), the Beijing Municipal Education Committee (KM200610005012), and the Beijing Municipal Foundation for Excellent Talents (20061D0501500211).

语种：英文

外文关键词：Gaze movement control; neural encoding and decoding; population cell coding; target search; visual context

摘要：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.