Hierarchical Classifier - Similar Models

Similar Models

One similar model is the notion of graphical models where an input space is systematically broken down into subspaces, and those into smaller subspaces, and so on, creating a hierarchy of input spaces. This allows for predictions about behavior of inputs in various regions with statistical methods such as Bayesian networks allowing for easily computable conditional probabilities. Recently, there has been a lot of research in this area with respect to vision systems. Hierarchical classifiers are extremely similar to these models, but do not have to depend on statistical interpretation.

Another similar model is the simple neural network. Commonly, neural networks are a network of individual nodes that each tries to learn a function of input to output. The functionality of the network as a whole is dependent on the ability of the nodes to work together to yield the correct overall output. Neural networks can be trained to do lots of tasks and are often domain-specific. However, as in the case of graphical models, neural networks have shown great general-purpose behavior in computer vision even when tackling relatively general problems. Hierarchical classifiers can, in fact, be seen as a special case of neural networks where, instead of learning functions, discrete output classes are learned. Learning is then a pattern-match with an error threshold instead of an interpolation of an approximate function.

Neuroscience's perspective on the workings of the human cortex also serves as a similar model. The generally accepted view of the brain today is that the brain is a generic pattern machine that works to abstract information again and again until it relates to a broad stored concept. For instance, a familiar face is not stored as a collection of pixels, rather as a combination of very specific eyes, nose, mouth, ears, etc. In this way, when the data has been classified into those components, that collection of those components can then be classified into that face. Thus, neuroscience trends and data are very valuable to research in these areas as they are highly relevant to the inner workings of these models. This is especially true since the human brain is inherently very good at applications like facial recognition that these models strive to be good at. The brain is in a sense a benchmark of proficiency for hierarchical processing.