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ICMCS
2010
IEEE

Comparison of clustering approaches for summarizing large populations of images

9 years 10 months ago
Comparison of clustering approaches for summarizing large populations of images
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...
Yushi Jing, Michele Covell, Henry A. Rowley
Added 09 Nov 2010
Updated 09 Nov 2010
Type Conference
Year 2010
Where ICMCS
Authors Yushi Jing, Michele Covell, Henry A. Rowley
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