GROUPING-BASED NONADDITIVE VERIFICATION

Verification is the final decision stage in many object recognition pr ocesses. It is carried out by evaluating a score for every hypothesis and choosing the hypotheses associated with the highest score. This pa per suggests a grouping-based verification paradigm, relying on the ob servation that a group of data features belonging to a hypothesized ob ject instance should be a ''good group.'' Therefore, it should support perceptual grouping information available from the image by grouping relations. The proposed score, which is the joint likelihood of these grouping cues, quantifies this observation in a probabilistic framewor k. Experiments with synthetic and real images show that the proposed m ethod performs better in difficult cases.