Comparison of Clustering Approaches for Summarizing Large Populations of Images
Venue
Proceedings ICME VCIDS, IEEE, Singapore (2010)
Publication Year
2010
Authors
Yushi Jing, Michele Covell, Henry A. Rowley
BibTeX
Abstract
This paper compares the efficacy and efficiency of different clustering approaches
for selecting a set of exemplar images, to present in the context of a semantic
concept. We evaluate these approaches using 900 diverse queries, each associated
with 1000 web images, and comparing the exemplars chosen by clustering to the top
20 images for that search term. Our results suggest that Affinity Propagation is
effective in selecting exemplars that match the top search images but at high
computational cost. We improve on these early results using a simple
distribution-based selection filter on incomplete clustering results. This
improvement allows us to use more computationally efficient approaches to
clustering, such as Hierarchical Agglomerative Clustering (HAC) and Partitioning
Around Medoids (PAM), while still reaching the same (or better) quality of results
as were given by Affinity Propagation in the original study. The computational
savings is significant since these alternatives are 7-27 times faster than Affinity
Propagation.
