Delayed reinforcement learning for adaptive image segmentation and feature extraction

Jing Peng, Bir Bhanu

Research output: Contribution to journalArticle

22 Scopus citations

Abstract

Object recognition is a multilevel process requiring a sequence of algorithms at low, intermediate, and high levels. Generally, such systems are open loop with no feedback between levels and assuring their robustness is a key challenge in computer vision and pattern recognition research. A robust closed-loop system based on "delayed" reinforcement learning is introduced in this paper. The parameters of a multilevel system employed for model-based object recognition are learned. The method improves recognition results over time by using the output at the highest level as feedback for the learning system. It has been experimentally validated by learning the parameters of image segmentation and feature extraction and thereby recognizing 2-D objects. The approach systematically controls feedback in a multilevel vision system and shows promise in approaching a long-standing problem in the fleld of computer vision and pattern recognition.

Original languageEnglish
Pages (from-to)482-488
Number of pages7
JournalIEEE Transactions on Systems, Man and Cybernetics Part C: Applications and Reviews
Volume28
Issue number3
DOIs
Publication statusPublished - 1 Dec 1998

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Keywords

  • Adaptive feature extraction
  • Adaptive image segmentation
  • Learning for multilevel vision
  • Learning in computer vision
  • Modelbased recognition
  • Multiscenario object recognition
  • Recognition feedback

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