Genetic evolution processing of data structures for image classification

Siu Yeung Cho, Zheru Chi

Research output: Journal PublicationArticlepeer-review

27 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)216-231
Number of pages16
JournalIEEE Transactions on Knowledge and Data Engineering
Volume17
Issue number2
DOIs
Publication statusPublished - Feb 2005
Externally publishedYes

Keywords

  • Adaptive processing of data structures
  • Genetic algorithm
  • Image classification
  • Neural networks

ASJC Scopus subject areas

  • Information Systems
  • Computer Science Applications
  • Computational Theory and Mathematics

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