  This paper describes the results of formally evaluating the MCV (Markov
concurrent vision) image labeling algorithm which is a (semi-) hierarchical
algorithm commencing with a partition made up of single pixel regions and
merging regions or subsets of regions using a Markov random field (MRF) image
model. It is an example of a general approach to computer vision called
concurrent vision in which the operations of image segmentation and image
classification are carried out concurrently. While many image labeling
algorithms output a single partition, or segmentation, the MCV algorithm
outputs a sequence of partitions and this more elaborate structure may provide
information that is valuable for higher level vision systems. With certain
types of MRF the component of the system for image evaluation can be
implemented as a hardwired feed forward neural network. While being applicable
to images (i.e. 2D signals), the algorithm is equally applicable to 1D signals
(e.g. speech) or 3D signals (e.g. video sequences) (though its performance in
such domains remains to be tested). The algorithm is assessed using subjective
and objective criteria with very good results.
