Constellation Model - Performance

Performance

The Constellation Model as conceived by Fergus et al. achieves successful categorization rates consistently above 90% on large datasets of motorbikes, faces, airplanes, and spotted cats. For each of these datasets, the Constellation Model is able to capture the "essence" of the object class in terms of appearance and/or shape. For example, face and motorbike datasets generate very tight shape models because objects in those categories have very well-defined structure, whereas spotted cats vary significantly in pose, but have a very distinctive spotted appearance. Thus, the model succeeds in both cases. It is important to note that the Constellation Model does not generally account for significant changes in orientation. Thus, if the model is trained on images of horizontal airplanes, it will not perform well on, for instance, images of vertically oriented planes unless the model is extended to account for this sort of rotation explicitly.

In terms of computational complexity, the Constellation Model is very expensive. If is the number of feature detections in the image, and the number of parts in the object model, then the hypothesis space is . Because the computation of sufficient statistics in the E-step of expectation maximization necessitates evaluating the likelihood for every hypothesis, learning becomes a major bottleneck operation. For this reason, only values of have been used in practical applications, and the number of feature detections is usually kept within the range of about 20-30 per image.