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A Sparse Object Category Model for Efficient Learning and Complete Recognition
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IV Recognition of Object Categories with Geometric Relations
A Sparse Object Category Model for Efficient Learning and Complete Recognition
Rob Fergus1  , Pietro Perona2 and Andrew Zisserman1
| (1) |
Dept. of Engineering Science, University of Oxford, Parks Road, Oxford, OX1 3PJ, U.K. |
| (2) |
Dept. of Electrical Engineering, California Institute of Technology, MC 136–93, Pasadena, CA 91125, U.S.A. |
Abstract
We present a “parts and structure” model for object category recognition that can be learnt efficiently and in a weakly-supervised
manner: the model is learnt from example images containing category instances, without requiring segmentation from background
clutter.
The model is a sparse representation of the object, and consists of a star topology configuration of parts modeling the output
of a variety of feature detectors. The optimal choice of feature types (whose repertoire includes interest points, curves
and regions) is made automatically.
In recognition, the model may be applied efficiently in a complete manner, bypassing the need for feature detectors, to give
the globally optimal match within a query image. The approach is demonstrated on a wide variety of categories, and delivers
both successful classification and localization of the object within the image.
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