An evolutionary autonomous agents approach to image feature extraction

Jiming Liu*, Y. Y. Tang, Y. C. Cao

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review

112 Citations (Scopus)


This paper presents a new approach to image feature extraction which utilizes evolutionary autonomous agents. Image features are often mathematically defined in terms of the graylevel intensity at image pixels. The optimality of image feature extraction is to find all the feature pixels from the image. In the proposed approach, the autonomous agents, being distributed computational entities, operate directly in the 2-D lattice of a digital image and exhibit a number of reactive behaviors. To effectively locate the feature pixels, individual agents sense the local stimuli from their image environment by means of evaluating the gray-level intensity of locally connected pixels, and accordingly activate their behaviors. The behavioral repository of the agents consists of: 1) feature-marking at local pixels and self-reproduction of offspring agents in the neighboring regions if the local stimuli are found to satisfy feature conditions, 2) diffusion to adjacent image regions if the feature conditions are not held, or 3) death if the agents exceed their life span. As part of the behavior evolution, the directions in which the agents selfreproduce and/or diffuse are inherited from the directions of their selected high-fitness parents. Here the fitness of a parent agent is defined according to the steps that the agent takes to locate an image feature pixel.

Original languageEnglish
Pages (from-to)141-158
Number of pages18
JournalIEEE Transactions on Evolutionary Computation
Issue number2
Publication statusPublished - Jul 1997

Scopus Subject Areas

  • Software
  • Theoretical Computer Science
  • Computational Theory and Mathematics

User-Defined Keywords

  • Autonomous agents
  • Diffusion
  • Evolutionary computation
  • Image feature extraction
  • Self-reproduction


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