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An accurate HMM-based similarity measure between finite sets of histograms

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Abstract

Histogram analysis has nowadays gain in interest, and a lot of work yet address this task. In most of the existing approaches, histograms are manipulated as simple vectors or as statistic distributions. As a consequence, only the bin values of the histograms are mostly considered and the histograms visual shapes are generally neglected. In this paper, hidden Markov models (HMMs) are associated with finite sets of histograms to capture both: the bin values and the visual shapes of the histograms contained in these sets, regardless of their bin sizes. The similarity rate between these HMMs is then used to compare two finite sets of histograms. Experimented in several areas within and beyond machine learning, the proposed approach exhibited relevant performances which outperformed the existing work in the hierarchical classification of the databases GTZAN+ and Corel.

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Authors and Affiliations

  1. Higher Teachers’ Training College, Department of Computer Science, University of Maroua, P.O.box 55, Maroua, Cameroon

    Sylvain Iloga

  2. UMR 8051, ETIS Laboratory, CNRS, ENSEA, University of Cergy-Pontoise, 6 avenue du Ponceau, 95000, Cergy, France

    Sylvain Iloga & Olivier Romain

  3. IRD UMI 209, UMMISCO-LIRIMA Laboratory, University of Yaoundé 1, P.O.box 337, Yaounde, Cameroon

    Sylvain Iloga & Maurice Tchuenté

Authors
  1. Sylvain Iloga
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Correspondence to Sylvain Iloga.

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Iloga, S., Romain, O. & Tchuenté, M. An accurate HMM-based similarity measure between finite sets of histograms. Pattern Anal Applic 22, 1079–1104 (2019). https://doi.org/10.1007/s10044-018-0734-z

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