What Makes a Good Feature?
Abstract
Perceptual information processing systems, both biological and non- biological, often consist of very elaborate algorithms designed to extract certain features or events from the input sensory array. Such features in vision range from simple 'on-off' units to 'hand' or 'face' detectors, and are now almost countless, so many having already been discovered or in use with no obvious limit in sight. Here we attempt to place some bounds upon just what features are worth computing. Previously, others have proposed that useful features reflect 'non-accidental' or 'suspicious' configurations that are especially informative yet typical of the world (such as two parallel lines). Using a Bayesian framework, we show how these intuitions can be made more precise, and in the process show that useful feature based inferences are highly dependent upon the context in which a feature is observed. For example, an inference supported by a feature at an early stage of processing when the context is relatively open may be nonsense in a more specific context provided by subsequent 'higher-level' processing. Therefore, specification for a 'good feature' requires a specification of the model class that sets the current context. We propose a general form for the structure of a model class, and use this structure as a basis for enumerating and evaluating appropriate 'good features'. Our conclusion is that one's cognitive capacities and goals are as important a part of 'good features' as are the regularities of the world.
Document Details
- Document Type
- Technical Report
- Publication Date
- Apr 01, 1992
- Accession Number
- ADA259962
Entities
People
- A. Jepson
- W. Richards
Organizations
- Massachusetts Institute of Technology