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Titolo:
Toward improved ranking metrics
Autore:
Sebe, N; Lew, MS; Huijsmans, DP;
Indirizzi:
Leiden Inst Adv Comp Sci, NL-2333 CA Leiden, Netherlands Leiden Inst Adv Comp Sci Leiden Netherlands NL-2333 CA iden, Netherlands
Titolo Testata:
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
fascicolo: 10, volume: 22, anno: 2000,
pagine: 1132 - 1143
SICI:
0162-8828(200010)22:10<1132:TIRM>2.0.ZU;2-L
Fonte:
ISI
Lingua:
ENG
Soggetto:
STEREO ALGORITHM; COLOR;
Keywords:
maximum likelihood; ranking metrics; content-based retrieval; color indexing; stereo matching; motion tracking;
Tipo documento:
Article
Natura:
Periodico
Settore Disciplinare:
Engineering, Computing & Technology
Citazioni:
30
Recensione:
Indirizzi per estratti:
Indirizzo: Sebe, N Leiden Inst Adv Comp Sci, Niels Bohrweg 1, NL-2333 CA Leiden, Netherlands Leiden Inst Adv Comp Sci Niels Bohrweg 1 Leiden Netherlands NL-2333 CA
Citazione:
N. Sebe et al., "Toward improved ranking metrics", IEEE PATT A, 22(10), 2000, pp. 1132-1143

Abstract

In many computer vision algorithms, a metric or similarity measure is usedto determine the distance between two features. The Euclidean or SSD (sum of the squared differences) metric is prevalent and justified from a maximum likelihood perspective when the additive noise distribution is Gaussian. Based on real noise distributions measured from international test sets, wehave found that the Gaussian noise distribution assumption is often invalid. This implies that other metrics, which have distributions closer to the real noise distribution, should be used. In this paper, we consider three different applications: content-based retrieval in image databases, stereo matching, and motion tracking. In each of them, we experiment with differentmodeling functions for the noise distribution and compute the accuracy of the methods using the corresponding distance measures. In our experiments, we compared the SSD metric, the SAD (sum of the absolute differences) metric, the Cauchy metric, and the Kullback relative information. For several algorithms from the research literature which used the SSD or SAD, we showed that greater accuracy could be obtained by using the Cauchy metric instead.

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Documento generato il 02/04/20 alle ore 18:55:41