A reinforcement agent for threshold fusion

Maryam Shokri, Hamid R. Tizhoosh

Research output: Contribution to journalArticlepeer-review


Finding an optimal threshold in order to segment digital images is a difficult task in image processing. Numerous approaches to image thresholding already exist in the literature. In this work, a reinforced threshold fusion for image binarization is introduced which aggregates existing thresholding techniques. The reinforcement agent learns the optimal weights for different thresholds and segments the image globally. A fuzzy reward function is employed to measure object similarities between the binarized image and the original gray-level image, and provide feedback to the agent. The experiments show that promising improvement can be obtained. Three well-established thresholding techniques are combined by the reinforcement agent and the results are compared using error measurements based on ground-truth images.

Original languageEnglish (US)
Pages (from-to)174-181
Number of pages8
JournalApplied Soft Computing Journal
Issue number1
StatePublished - Jan 2008


  • Image thresholding
  • Reinforcement learning
  • Threshold fusion

ASJC Scopus subject areas

  • Software


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