There has recently been a significant interest in the
organization and content-based querying of large images
databases. Most frequently, the underlying hypothesis is that image
similarity can be characterized by low-level image features, without
further abstraction. This assumes that there is sufficient agreement
between machine and human measures of image similarity for the database
to be useful. We wish to assess the veracity of this assumption. To
this end, we develop measures of the agreement between two
partitionings of an image set; we show that it is vital to take chance
agreements into account. We then use these measures to assess the
agreement between human subjects and a variety of machine clustering
techniques on a set of images. The results can be used to select and
refine image distance measures for querying and organizing image
databases.