A Sparse Object Category Model for Efficient Learning and Complete Recognition

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.

• Type: Conference
• Year: 2006
• Where: CLOR
• Authors: Robert Fergus, Pietro Perona, Andrew Zisserman
• Keywords: CLOR 2006, Computer Vision, Feature Detectors, Model, Object Category Recognition