Open Access

Correction of Misclassifications Using a Proximity-Based Estimation Method

  • Antti Niemistö1, 2Email author,
  • Ilya Shmulevich2,
  • Vladimir V. Lukin3,
  • Alexander N. Dolia3, 4 and
  • Olli Yli-Harja1
EURASIP Journal on Advances in Signal Processing20042004:508513

Received: 14 October 2003

Published: 8 July 2004


An estimation method for correcting misclassifications in signal and image processing is presented. The method is based on the use of context-based (temporal or spatial) information in a sliding-window fashion. The classes can be purely nominal, that is, an ordering of the classes is not required. The method employs nonlinear operations based on class proximities defined by a proximity matrix. Two case studies are presented. In the first, the proposed method is applied to one-dimensional signals for processing data that are obtained by a musical key-finding algorithm. In the second, the estimation method is applied to two-dimensional signals for correction of misclassifications in images. In the first case study, the proximity matrix employed by the estimation method follows directly from music perception studies, whereas in the second case study, the optimal proximity matrix is obtained with genetic algorithms as the learning rule in a training-based optimization framework. Simulation results are presented in both case studies and the degree of improvement in classification accuracy that is obtained by the proposed method is assessed statistically using Kappa analysis.

Keywords and phrases

misclassification correctionimage recognitiontraining-based optimizationgenetic algorithmsmusical key findingremote sensing

Authors’ Affiliations

Institute of Signal Processing, Tampere University of Technology
Department of Pathology, The University of Texas M.D. Anderson Cancer Center
Department 504, National Aerospace University
School of Electronics and Computer Science, University of Southampton


© Niemistö et al. 2004