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  Learning to Localize Objects with Structured Output Regression

Blaschko, M., & Lampert, C. (2008). Learning to Localize Objects with Structured Output Regression. In A. Forsyth, P. Torr, & A. Zisserman (Eds.), Computer Vision – ECCV 2008: 10th European Conference on Computer Vision, Marseille, France, October 12-18, 2008 (pp. 2-15). Berlin, Germany: Springer.

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Datensatz-Permalink: https://hdl.handle.net/11858/00-001M-0000-0013-C6C3-1 Versions-Permalink: https://hdl.handle.net/21.11116/0000-0003-37E2-0
Genre: Konferenzbeitrag

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externe Referenz:
https://link.springer.com/content/pdf/10.1007%2F978-3-540-88682-2_2.pdf (Verlagsversion)
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Urheber

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 Urheber:
Blaschko, MB1, 2, Autor           
Lampert, CH1, 2, Autor           
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              

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Schlagwörter: -
 Zusammenfassung: Sliding window classifiers are among the most successful and widely applied techniques for object localization. However, training is typically done in a way that is not specific to the localization task. First a binary classifier is trained using a sample of positive and negative examples, and this classifier is subsequently applied to multiple regions within test images. We propose instead to treat object localization in a principled way by posing it as a problem of predicting structured data: we model the problem not as binary classification, but as the prediction of the bounding box of objects located in images. The use of a joint-kernel framework allows us to formulate the training procedure as a generalization of an SVM, which can be solved efficiently. We further improve computational efficiency by using a branch-and-bound strategy for localization during both training and testing. Experimental evaluation on the PASCAL VOC and TU Darmstadt datasets show that the structured training procedure improves pe
rformance over binary training as well as the best previously published scores.

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Sprache(n):
 Datum: 2008-10
 Publikationsstatus: Erschienen
 Seiten: -
 Ort, Verlag, Ausgabe: -
 Inhaltsverzeichnis: -
 Art der Begutachtung: -
 Identifikatoren: DOI: 10.1007/978-3-540-88682-2_2
BibTex Citekey: 5247
 Art des Abschluß: -

Veranstaltung

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Titel: 10th European Conference on Computer Vision (ECCV 2008)
Veranstaltungsort: Marseille, France
Start-/Enddatum: 2008-10-12 - 2008-10-18

Entscheidung

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Projektinformation

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Quelle 1

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Titel: Computer Vision – ECCV 2008: 10th European Conference on Computer Vision, Marseille, France, October 12-18, 2008
Genre der Quelle: Konferenzband
 Urheber:
Forsyth, AD, Herausgeber
Torr, PHS, Herausgeber
Zisserman, A, Herausgeber
Affiliations:
-
Ort, Verlag, Ausgabe: Berlin, Germany : Springer
Seiten: - Band / Heft: - Artikelnummer: - Start- / Endseite: 2 - 15 Identifikator: ISBN: 978-3-540-88681-5

Quelle 2

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Titel: Lecture Notes in Computer Science
Genre der Quelle: Reihe
 Urheber:
Affiliations:
Ort, Verlag, Ausgabe: -
Seiten: - Band / Heft: 5304 Artikelnummer: - Start- / Endseite: - Identifikator: -