Statistical testing of segment homogeneity in classification of piecewise-regular objects

• Autorzy: Savchenko A. V. , Belova N. S.

Identyfikatory

DOI

10.1515/amcs-2015-0065

• Języki publikacji: EN

Abstrakty

EN

The paper is focused on the problem of multi-class classification of composite (piecewise-regular) objects (e.g., speech signals, complex images, etc.). We propose a mathematical model of composite object representation as a sequence of independent segments. Each segment is represented as a random sample of independent identically distributed feature vectors. Based on this model and a statistical approach, we reduce the task to a problem of composite hypothesis testing of segment homogeneity. Several nearest-neighbor criteria are implemented, and for some of them the well-known special cases (e.g., the Kullback–Leibler minimum information discrimination principle, the probabilistic neural network) are highlighted. It is experimentally shown that the proposed approach improves the accuracy when compared with contemporary classifiers.

Słowa kluczowe

EN

statistical pattern recognition; testing of segment homogeneity; probabilistic neural network

PL

rozpoznawanie obrazu; jednorodność segmentu; probabilistyczna sieć neuronowa

• Wydawca: Oficyna Wydawnicza Uniwersytetu Zielonogórskiego
• Czasopismo: International Journal of Applied Mathematics and Computer Science
• Rocznik: 2015
• Tom: Vol. 25, no. 4
• Strony: 915--925
• Opis fizyczny: Bibliogr. 31 poz., rys., tab., wykr.

Twórcy

autor

Savchenko A. V.

• Laboratory of Algorithms and Technologies for Network Analysis, National Research University Higher School of Economics, 136 Rodionova St., Nizhny Novgorod 603093, Russia

autor

Belova N. S.

• Faculty of Computer Science, National Research University Higher School of Economics, 20 Myasnitskaya St., Moscow 101000, Russia

Bibliografia

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