Principle of Representational Minimum Description Length in Image Analysis and Pattern Recognition

摘要

Problems of decision criteria in tasks of image analysis and pattern recognition are considered. Overlearning as a practical consequence of fundamental paradoxes in inductive inference is illustrated by examples. Theoretical (based on algorithmic complexity) and practical formulations of the minimum description length (MDL) principle are given. A decrease in the overlearning effect is shown on examples of modern recognition, grouping, and segmentation methods modified by the MDL principle. The representational MDL principle is introduced as an extension of the MDL principle, which makes it possible to take into account the dependence of the optimality criterion of the model from prior information given in data representation, as well as to perform optimization of representations. Novel possibilities of constructing learnable image analysis algorithms by optimizing the representation based on the extended MDL principle are described.