Learning a similarity-based distance measure for image database organization from human partitionings of an image set

Abstract

In this paper we employ human judgments of image similarity to improve the organization of an image database. We first derive a statistic, κB which measures the agreement between two partitionings of an image set. κB is used to assess agreement both amongst and between human and machine partitionings. This provides a rigorous means of choosing between competing image database organization systems, and of assessing the performance of such systems with respect to human judgments. Human partitionings of an image set are used to define a similarity value based on the frequency with which images are judged to be similar. When this measure is used to partition an image set using a clustering techn..