Deutsch
 
Hilfe Datenschutzhinweis Impressum
  DetailsucheBrowse

Datensatz

DATENSATZ AKTIONENEXPORT
  Mixtures of Conditional Gaussian Scale Mixtures Applied to Multiscale Image Representations

Theis, L., Hosseini, R., & Bethge, M. (2012). Mixtures of Conditional Gaussian Scale Mixtures Applied to Multiscale Image Representations. PLoS One, 7(7), 1-8. doi:10.1371/journal.pone.0039857.

Item is

Basisdaten

einblenden: ausblenden:
Genre: Zeitschriftenartikel

Externe Referenzen

einblenden:

Urheber

einblenden:
ausblenden:
 Urheber:
Theis, L1, 2, Autor           
Hosseini, R1, 2, Autor           
Bethge, M1, 2, Autor           
Affiliations:
1Research Group Computational Vision and Neuroscience, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497805              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              

Inhalt

einblenden:
ausblenden:
Schlagwörter: -
 Zusammenfassung: We present a probabilistic model for natural images that is based on mixtures of Gaussian scale mixtures and a simple multiscale representation. We show that it is able to generate images with interesting higher-order correlations when trained on natural images or samples from an occlusion-based model. More importantly, our multiscale model allows for a principled evaluation. While it is easy to generate visually appealing images, we demonstrate that our model also yields the best performance reported to date when evaluated with respect to the cross-entropy rate, a measure tightly linked to the average log-likelihood. The ability to quantitatively evaluate our model differentiates it from other multiscale models, for which evaluation of these kinds of measures is usually intractable.

Details

einblenden:
ausblenden:
Sprache(n):
 Datum: 2012-07
 Publikationsstatus: Online veröffentlicht
 Seiten: -
 Ort, Verlag, Ausgabe: -
 Inhaltsverzeichnis: -
 Art der Begutachtung: -
 Identifikatoren: DOI: 10.1371/journal.pone.0039857
eDoc: e39857
 Art des Abschluß: -

Veranstaltung

einblenden:

Entscheidung

einblenden:

Projektinformation

einblenden:

Quelle 1

einblenden:
ausblenden:
Titel: PLoS One
Genre der Quelle: Zeitschrift
 Urheber:
Affiliations:
Ort, Verlag, Ausgabe: San Francisco, CA : Public Library of Science
Seiten: - Band / Heft: 7 (7) Artikelnummer: - Start- / Endseite: 1 - 8 Identifikator: ISSN: 1932-6203
CoNE: https://pure.mpg.de/cone/journals/resource/1000000000277850