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Pattern Analysis and Applications
Erschienen in: Pattern Analysis and Applications 3/2019

30.07.2018 | Theoretical Advances

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

verfasst von: Sylvain Iloga, Olivier Romain, Maurice Tchuenté

Erschienen in: Pattern Analysis and Applications | Ausgabe 3/2019

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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.
Vorheriger Artikel Fuzzy chromatic co-occurrence matrices for tracking objects
Nächster Artikel Invariant color images representation using accurate quaternion Legendre–Fourier moments

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Metadaten
Titel
An accurate HMM-based similarity measure between finite sets of histograms
verfasst von
Sylvain Iloga
Olivier Romain
Maurice Tchuenté
Publikationsdatum
30.07.2018
Verlag
Springer London
Erschienen in
Pattern Analysis and Applications / Ausgabe 3/2019
Print ISSN: 1433-7541
Elektronische ISSN: 1433-755X
DOI
https://doi.org/10.1007/s10044-018-0734-z

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