Shape-based object recognition via Evidence Accumulation Inference

• We proposed a structural representation of shapes using Bayesian Network.
• We proposed a way of Evidence Accumulation Inference to pick up region of interest.
• Our method achieves the state-of-the-art performance on ETHZ shape classes.

Shape-based object recognition is one of the most challenging problems in computer vision. Learning a structural representation using graphical models is a new trend in object recognition. This paper tries to apply graphical models to learn a shape representation and proposes a pipeline of shape-based object recognition. First, a Bayesian Network represents the shape knowledge of a type of object. Second, an Evidence Accumulation Inference with Bayesian Network is developed to search for the region of interest which is most likely to contain an object in an image. Finally, a spatial pyramid matching approach is used to verify the hypothesis to identify objects and to refine object locations. Our experiments corroborate that Evidence Accumulation Inference with Bayesian Network for object recognition is correct and show that the proposed pipeline achieves comparable results on well-known ETHZ shape classes and INRIA Horse dataset.