Traditionally, the goal of image segmentation has been to produce a single partition of an image. This partition is compared to some 'ground truth', or human approved partition, to evaluate the performance of the algorithm. This paper utilizes a framework for considering a range of possible partitions of the image to compute a probability distribution on the space of possible partitions of the image. This is an important distinction from the traditional model of segmentation, and has many implications in the integration of segmentation and recognition research. The probabilistic framework that enables us to return a confidence measure on each result also allows us to discard from consideration entire classes of results due to their low cumulative probability. The distributions thus returned may be passed to higher-level algorithms to better enable them to interpret the segmentation results. Several experimental results are presented using Markov random fields as texture models to generate distributions of segments and segmentations on textured images. Both simple homogeneous images and natural scenes are presented.
- Bayesian methods
ASJC Scopus subject areas
- Signal Processing
- Computer Vision and Pattern Recognition