Image object classification using saccadic search, spatio-temporal pattern encoding and self-organisation

被引：6

作者：

Becanovic, V ^{[1
]}

机构：

[1] George Mason Univ, Inst Biosci Bioinformat & Biotechnol, Fairfax, VA 22030 USA

来源：

PATTERN RECOGNITION LETTERS | 2000年 / 21卷 / 03期

关键词：

saccadic eye movement; foveation; segmentation; PCNN time-series; signatures; hierarchical SOM;

D O I：

10.1016/S0167-8655(99)00154-3

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

A method for extracting features from photographic images is investigated. The input image is through a saccadic search algorithm divided into a set of sub-images, segmented and coded by a spatio-temporal encoding engine. The input image is thus represented by a set of characteristic pattern signatures, well suited for classification by an unsupervised neural network. A strategy using multiple self-organising feature maps (SOM) in a hierarchical manner is used. With this approach, using a certain degree of user selection, a database of sub-images is grouped according to similarities in signature space. (C) 2000 Elsevier Science B.V. All rights reserved.

引用

页码：253 / 263

页数：11

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