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Titolo: Toward improved ranking metrics
Autore: Sebe, N; Lew, MS; Huijsmans, DP;
 Indirizzi:
 Leiden Inst Adv Comp Sci, NL2333 CA Leiden, Netherlands Leiden Inst Adv Comp Sci Leiden Netherlands NL2333 CA iden, Netherlands
 Titolo Testata:
 IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
fascicolo: 10,
volume: 22,
anno: 2000,
pagine: 1132  1143
 SICI:
 01628828(200010)22:10<1132:TIRM>2.0.ZU;2L
 Fonte:
 ISI
 Lingua:
 ENG
 Soggetto:
 STEREO ALGORITHM; COLOR;
 Keywords:
 maximum likelihood; ranking metrics; contentbased 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, NL2333 CA Leiden, Netherlands Leiden Inst Adv Comp Sci Niels Bohrweg 1 Leiden Netherlands NL2333 CA



 Citazione:
 N. Sebe et al., "Toward improved ranking metrics", IEEE PATT A, 22(10), 2000, pp. 11321143
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: contentbased 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.
ASDD Area Sistemi Dipartimentali e Documentali, Università di Bologna, Catalogo delle riviste ed altri periodici
Documento generato il 02/04/20 alle ore 18:55:41