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Classification of a Sequence of Objects with the Fuzzy Decoding Method

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Rough Sets and Current Trends in Computing (RSCTC 2014)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8536))

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Abstract

The problem of recognition of a sequence of objects (e.g., video-based image recognition, phoneme recognition) is explored. The generalization of the fuzzy phonetic decoding method is proposed by assuming the distribution of the classified object to be of exponential type. Its preliminary phase includes association of each model object with the fuzzy set of model classes with grades of membership defined as the confusion probabilities estimated with the Kullback-Leibler divergence between model distributions. At first, each object (e.g., frame) in a classified sequence is put in correspondence with the fuzzy set which grades are defined as the posterior probabilities. Next, this fuzzy set is intersected with the fuzzy set corresponding to the nearest neighbor. Finally, the arithmetic mean of these fuzzy intersections is assigned to the decision for the whole sequence. In this paper we propose not to limit the method’s usage with the Kullback-Leibler discrimination and to estimate the grades of membership of models and query objects based on an arbitrary distance with appropriate scale factor. The experimental results in the problem of isolated Russian vowel phonemes and words recognition for state-of-the-art measures of similarity are presented. It is shown that the correct choice of the scale parameter can significantly increase the recognition accuracy.

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Author information

Authors and Affiliations

  1. National Research University Higher School of Economics, Nizhny Novgorod, Russia

    Andrey V. Savchenko

  2. Nizhny Novgorod State Linguistic University, Russia

    Lyudmila V. Savchenko

Authors
  1. Andrey V. Savchenko
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  2. Lyudmila V. Savchenko
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Editor information

Editors and Affiliations

  1. Department of Computer Science and Artificial Intelligence, University of Granada, Calle del Periodista Daniel Saucedo Aranda s/n, 18071, Granada, Spain

    Chris Cornelis

  2. Institute of Computer Science, Warsaw University of Technology, Nowowiejska 15/19, 00-665, Warsaw, Poland

    Marzena Kryszkiewicz

  3. University of Warsaw, Poland

    Dominik Ślȩzak

  4. Polytechnic University of Madrid, Spain

    Ernestina Menasalvas Ruiz

  5. Deparment of Computer Sciences, Universidad Central Marta Abreu de las Villas, Santa Clara, Villa Clara, Cuba

    Rafael Bello

  6. Department of Computer Science and Technology, Nanjing University, 210023, Nanjing, China

    Lin Shang

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© 2014 Springer International Publishing Switzerland

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Savchenko, A.V., Savchenko, L.V. (2014). Classification of a Sequence of Objects with the Fuzzy Decoding Method. In: Cornelis, C., Kryszkiewicz, M., Ślȩzak, D., Ruiz, E.M., Bello, R., Shang, L. (eds) Rough Sets and Current Trends in Computing. RSCTC 2014. Lecture Notes in Computer Science(), vol 8536. Springer, Cham. https://doi.org/10.1007/978-3-319-08644-6_32

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  • DOI: https://doi.org/10.1007/978-3-319-08644-6_32

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-08643-9

  • Online ISBN: 978-3-319-08644-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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