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Proceedings Paper

Coherent image layout using an adaptive visual vocabulary
Author(s): Scott E. Dillard; Michael J. Henry; Shawn Bohn; Luke J. Gosink
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Paper Abstract

When querying a huge image database containing millions of images, the result of the query may still contain many thousands of images that need to be presented to the user. We consider the problem of arranging such a large set of images into a visually coherent layout, one that places similar images next to each other. Image similarity is determined using a bag-of-features model, and the layout is constructed from a hierarchical clustering of the image set by mapping an in-order traversal of the hierarchy tree into a space-filling curve. This layout method provides strong locality guarantees so we are able to quantitatively evaluate performance using standard image retrieval benchmarks. Performance of the bag-of-features method is best when the vocabulary is learned on the image set being clustered. Because learning a large, discriminative vocabulary is a computationally demanding task, we present a novel method for efficiently adapting a generic visual vocabulary to a particular dataset. We evaluate our clustering and vocabulary adaptation methods on a variety of image datasets and show that adapting a generic vocabulary to a particular set of images improves performance on both hierarchical clustering and image retrieval tasks.

Paper Details

Date Published: 6 March 2013
PDF: 14 pages
Proc. SPIE 8661, Image Processing: Machine Vision Applications VI, 86610Q (6 March 2013); doi: 10.1117/12.2004733
Show Author Affiliations
Scott E. Dillard, Pacific Northwest National Lab. (United States)
Michael J. Henry, Pacific Northwest National Lab. (United States)
Shawn Bohn, Pacific Northwest National Lab. (United States)
Luke J. Gosink, Pacific Northwest National Lab. (United States)

Published in SPIE Proceedings Vol. 8661:
Image Processing: Machine Vision Applications VI
Philip R. Bingham; Edmund Y. Lam, Editor(s)

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