Constructing simple stable descriptions for image partitioning

A new formulation of the image partitioning problem is presented: construct a complete and stable description of an image-in terms of a specified descriptive language-that is simplest in the sense of being shortest, which yields intuitively satisfying partitions for a wide class of images.

Abstract

A new formulation of the image partitioning problem is presented: construct a complete and stable description of an image-in terms of a specified descriptive language-that is simplest in the sense of being shortest. We show that a descriptive language limited to a low-order polynomial description of the intensity variation within each region and a chain-code-like description of the region boundaries yields intuitively satisfying partitions for a wide class of images. The advantage of this formulation is that it can be extended to deal with subsequent steps of the image understanding problem (or to deal with other attributes, such as texture) in a natural way by augmenting the descriptive language. Experiments performed on a variety of both real and synthetic images demonstrate the superior performance of this approach over partitioning techniques based on clustering vectors of local image attributes and standard edge-detection techniques.