Contextual performance prediction for low-level image analysis algorithms

This paper explores a generic approach to predict the output accuracy of an algorithm without running it, by a careful examination of the local context. Such a performance prediction will allow one to qualify the appropriateness of an algorithm to treat images with given properties (contrast, resolution, noise, richness in details, contours or textures, etc.) resulting either from experimental acquisition conditions or from a specific type of scene. We have to answer the following question: a context c being given at any site, what will be the performance? In our experiments, c is described by three contextual variables: Gabor components, entropy and signal noise ratio. As initially proposed in the related work of Chalmond and Graffigne (1999), the prediction function is determined from training using a logistic regression model. This technique is illustrated on aerial infrared images for two types of algorithm: edge detection and displacement estimation