Object Recognition Using Reflex Fuzzy Min-Max Neural Network with Floating Neurons

  • A. V. Nandedkar
  • P. K. Biswas
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4338)


This paper proposes an object recognition system that is invariant to rotation, translation and scale and can be trained under partial supervision. The system is divided into two sections namely, feature extraction and recognition sections. Feature extraction section uses proposed rotation, translation and scale invariant features. Recognition section consists of a novel Reflex Fuzzy Min-Max Neural Network (RFMN) architecture with “Floating Neurons”. RFMN is capable to learn mixture of labeled and unlabeled data which enables training under partial supervision. Learning under partial supervision is of high importance for the practical implementation of pattern recognition systems, as it may not be always feasible to get a fully labeled dataset for training or cost to label all samples is not affordable. The proposed system is tested on shape data-base available online, Marathi and Bengali digits. Results are compared with “General Fuzzy Min-Max Neural Network” proposed by Gabrys and Bargiela.


Activation Function Object Recognition Unlabeled Data Pattern Recognition System Class Node 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • A. V. Nandedkar
    • 1
  • P. K. Biswas
    • 1
  1. 1.Electronics & Elec. Communication Engg. DepartmentIndian Institute of TechnologyKharagpurIndia

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