Minimum Description Length Principle in the Field of Image Analysis and Pattern Recognition

摘要

Problems of decision criterion in the tasks of image analysis and pattern recognition are consid ered. Overlearning as a practical consequence of fundamental paradoxes in inductive inference is illustrated with examples. Theoretical (on the base of algorithmic complexity) and practical formulations of the mini mum description length (MDL) principle are given. Decrease of the overlearning effect is shown in the exam ples of modern recognition, grouping, and segmentation methods modified with the MDL principle. Novel possibilities of construction of learnable image analysis algorithms by representation optimization on the base of the MDL principle are described.