Pattern recognition using higher-order local autocorrelation coefficients

POPOVICI, Vlad a JP THIRAN. Pattern recognition using higher-order local autocorrelation coefficients. Pattern Recognition Letters. Amsterdam: Elsevier, 2004, roč. 25, č. 10, s. 1107-1113. ISSN 0167-8655. Dostupné z: https://doi.org/10.1016/j.patrec.2004.03.007.

Základní údaje

Originální název

Pattern recognition using higher-order local autocorrelation coefficients

Autoři

POPOVICI, Vlad a JP THIRAN

Vydání

Pattern Recognition Letters, Amsterdam, Elsevier, 2004, 0167-8655

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Další údaje

Jazyk

angličtina

Typ výsledku

Článek v odborném periodiku

Utajení

není předmětem státního či obchodního tajemství

Impakt faktor

Impact factor: 0.576

Označené pro přenos do RIV

Ne

DOI

https://doi.org/10.1016/j.patrec.2004.03.007

UT WoS

000222392000002

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Anotace

V originále

The autocorrelations have been previously used as features for 1D or 2D signal classification in a wide range of applications, like texture classification, face detection and recognition, EEG signal classification, and so on. However, in almost all the cases, the high computational costs have hampered the extension to higher orders (more than the second order). In this paper we present an effective method for using higher order autocorrelation functions for pattern recognition. We will show that while the autocorrelation feature vectors (described below) are elements of a high dimensional space, one may avoid their explicit computation when the method employed can be expressed in terms of inner products of input vectors. Different typical scenarios of using the autocorrelations will be presented and we will show that the order of autocorrelations is no longer an obstacle. (C) 2004 Elsevier B.V. All rights reserved.