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Different Orderings and Visual Sequence Alignment Algorithms for Image Classification

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Artificial Intelligence and Soft Computing (ICAISC 2014)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8467))

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Abstract

This paper presents a successful connection of different sequence alignment algorithms with Bag of Visual Words concept for image classification. In particular, sequences were created on the basis of dense SIFT descriptors, for which different types of sequence orderings were proposed. Then, the similarities between images were calculated with two different sequence alignment algorithms. Finally, the SVM algorithm was proposed as a classifier. The obtained results showed that both sequence alignment algorithms obtain very similar results and that the type of ordering affects the accuracy very slightly.

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Authors and Affiliations

  1. Department of Mathematics and Computer Sciences, University of Warmia and Mazury, Olsztyn, Poland

    Paweł Drozda, Krzysztof Sopyła & Przemysław Górecki

Authors
  1. Paweł Drozda
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  2. Krzysztof Sopyła
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  3. Przemysław Górecki
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Editor information

Editors and Affiliations

  1. Częstochowa University of Technology, Armii Krajowej 36, 42-200, Częstochowa, Poland

    Leszek Rutkowski , Marcin Korytkowski  & Rafał Scherer ,  & 

  2. AGH University of Science and Technology, Mickiewicza 30, 30-059, Kraków, Poland

    Ryszard Tadeusiewicz

  3. Computer Science Division, Department of Electrical Engineering and Computer Sciences, University of California Berkeley, 94720-1776, Berkeley, CA, USA

    Lotfi A. Zadeh

  4. Computational Intelligence Laboratory, Electrical and Computer Engineering, University of Louisville, 405 Lutz Hall, 40292, Louisville, KY, USA

    Jacek M. Zurada

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Drozda, P., Sopyła, K., Górecki, P. (2014). Different Orderings and Visual Sequence Alignment Algorithms for Image Classification. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing. ICAISC 2014. Lecture Notes in Computer Science(), vol 8467. Springer, Cham. https://doi.org/10.1007/978-3-319-07173-2_59

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  • DOI: https://doi.org/10.1007/978-3-319-07173-2_59

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-07172-5

  • Online ISBN: 978-3-319-07173-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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