Genetic evolution processing of data structures for image classification

被引：23

作者：

Cho, SY

Chi, ZR

机构：

[1] Nanyang Technol Univ, Sch Comp Engn, Div Comp Syst, Singapore 639798, Singapore

[2] Hong Kong Polytech Univ, Dept Elect & Informat Engn, Hong Kong, Hong Kong, Peoples R China

来源：

IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING | 2005年 / 17卷 / 02期

关键词：

adaptive processing of data structures; genetic algorithm; image classification; neural networks;

D O I：

10.1109/TKDE.2005.28

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper describes a method of structural pattern recognition based on a genetic evolution processing of data structures with neural networks representation. Conventionally, one of the most popular learning formulations of data structure processing is Backpropagation Through Structures (BPTS) [ 7]. The BPTS algorithm has been successfully applied to a number of learning tasks that involved structural patterns such as image, shape, and texture classifications. However, this BPTS typed algorithm suffers from the long-term dependency problem in learning very deep tree structures. In this paper, we propose the genetic evolution for this data structures processing. The idea of this algorithm is to tune the learning parameters by the genetic evolution with specified chromosome structures. Also, the fitness evaluation as well as the adaptive crossover and mutation for this structural genetic processing are investigated in this paper. An application to flowers image classification by a structural representation is provided for the validation of our method. The obtained results significantly support the capabilities of our proposed approach to classify and recognize flowers in terms of generalization and noise robustness.

引用

页码：216 / 231

页数：16

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