Abstract: A new distance function to compare arbitrary partitions is proposed. Clustering of image collections and image segmentation give objects to be matched. Offered metric intends for combination of visual features and metadata analysis to solve a semantic gap between low-level visual features and high-level human concept.

Keywords: partition, metric, clustering, image segmentation.

ACM Classification Keywords: I.5.3 Clustering - Similarity measures

Link:

PARTITION METRIC FOR CLUSTERING FEATURES ANALYSIS

Dmitry Kinoshenko, Vladimir Mashtalir, Vladislav Shlyakhov

http://www.foibg.com/ijita/vol14/ijita14-3-p06.pdf