Appearance Model Evaluation

Our approach to model evaluation is based on measuring, directly,
key properties of the model. To be effective, a model needs the
ability to generate a broad range of examples of the class of
images that have been modelled. We refer to this as
*Generalisation* ability. Although this property is
necessary , it is not sufficient. We also require that the model
can only generate examples that are consistent with the class of
images modelled. We refer to this as *Specificity*. We
define both of these measures by comparing the distribution of
training images and the distribution of images generated using the
model. An overview of the approach is given in Figure
2. Any image can be considered as a point in a
high-dimensional space (defined by it's intensity values). The
training set forms a cloud of points in such a space. If we
sample from the model, we generate a second cloud of points in
this space. For an ideal model, the two clouds are coincident. We
define *Generalisation* and *Specificity* in terms of the
distance from each training image to the nearest model-generated
image, and the distance from each model-generated image to the
nearest training image. We discuss the choice of an appropriate
distance metric in section 3.3.

[width = 0.85 ]../Graphics/hyperspace_example.png |