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Multiple object class detection with a generative model

Mikolajczyk, Krystian ; Leibe, Bastian ; Schiele, Bernt (2006)
Multiple object class detection with a generative model.
2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06). New York, NY (17.06.2006-22.06.2006)
doi: 10.1109/CVPR.2006.202
Konferenzveröffentlichung, Bibliographie

Kurzbeschreibung (Abstract)

In this paper we propose an approach capable of simultaneous recognition and localization of multiple object classes using a generative model. A novel hierarchical representation allows to represent individual images as well as various objects classes in a single, scale and rotation invariant model. The recognition method is based on a codebook representation where appearance clusters built from edge based features are shared among several object classes. A probabilistic model allows for reliable detection of various objects in the same image. The approach is highly efficient due to fast clustering and matching methods capable of dealing with millions of high dimensional features. The system shows excellent performance on several object categories over a wide range of scales, in-plane rotations, background clutter, and partial occlusions. The performance of the proposed multi-object class detection approach is competitive to state of the art approaches dedicated to a single object class recognition problem.

Typ des Eintrags: Konferenzveröffentlichung
Erschienen: 2006
Autor(en): Mikolajczyk, Krystian ; Leibe, Bastian ; Schiele, Bernt
Art des Eintrags: Bibliographie
Titel: Multiple object class detection with a generative model
Sprache: Englisch
Publikationsjahr: 2006
Ort: Los Alamitos, Calif.
Verlag: IEEE Computer Society
Buchtitel: 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06), Vol. 1
Veranstaltungstitel: 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06)
Veranstaltungsort: New York, NY
Veranstaltungsdatum: 17.06.2006-22.06.2006
DOI: 10.1109/CVPR.2006.202
Kurzbeschreibung (Abstract):

In this paper we propose an approach capable of simultaneous recognition and localization of multiple object classes using a generative model. A novel hierarchical representation allows to represent individual images as well as various objects classes in a single, scale and rotation invariant model. The recognition method is based on a codebook representation where appearance clusters built from edge based features are shared among several object classes. A probabilistic model allows for reliable detection of various objects in the same image. The approach is highly efficient due to fast clustering and matching methods capable of dealing with millions of high dimensional features. The system shows excellent performance on several object categories over a wide range of scales, in-plane rotations, background clutter, and partial occlusions. The performance of the proposed multi-object class detection approach is competitive to state of the art approaches dedicated to a single object class recognition problem.

Fachbereich(e)/-gebiet(e): 20 Fachbereich Informatik
20 Fachbereich Informatik > Multimodale Interaktive Systeme
Hinterlegungsdatum: 20 Nov 2008 08:25
Letzte Änderung: 28 Nov 2024 10:53
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