Pictorial Structures for Object Recognition

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Pictorial Structures for Object Recognition (2003)

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by Pedro F. Felzenszwalb , Daniel P. Huttenlocher

Venue:	IJCV
Citations:	815 - 15 self

BibTeX

@ARTICLE{Felzenszwalb03pictorialstructures,
    author = {Pedro F. Felzenszwalb and Daniel P. Huttenlocher},
    title = {Pictorial Structures for Object Recognition},
    journal = {IJCV},
    year = {2003},
    volume = {61},
    pages = {2005}
}

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Abstract

In this paper we present a statistical framework for modeling the appearance of objects. Our work is motivated by the pictorial structure models introduced by Fischler and Elschlager. The basic idea is to model an object by a collection of parts arranged in a deformable configuration. The appearance of each part is modeled separately, and the deformable configuration is represented by spring-like connections between pairs of parts. These models allow for qualitative descriptions of visual appearance, and are suitable for generic recognition problems. We use these models to address the problem of detecting an object in an image as well as the problem of learning an object model from training examples, and present efficient algorithms for both these problems. We demonstrate the techniques by learning models that represent faces and human bodies and using the resulting models to locate the corresponding objects in novel images.

Keyphrases

object recognition pictorial structure deformable configuration visual appearance statistical framework generic recognition problem object model spring-like connection novel image basic idea qualitative description pictorial structure model human body present efficient algorithm corresponding object

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