There is currently much interest in the organization and
content-based querying image databases. The usual hypothesis is
that image similarity can be characterized by low-level features,
without further abstraction. This assumes that agreement between
machine and human measures of similarity is sufficient for the database
to be useful. To assess this assumption, we develop measures of the
agreement between partitionings of an image set, showing that chance
agreements must be considered. These measures are used to
assess the agreement between human subjects and several machine
clustering techniques on an image set. The results can be used to
select and refine distance measures for querying and organizing image
databases.