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Proceedings Paper

iMap: a stable layout for navigating large image collections with embedded search
Author(s): Chaoli Wang; John P. Reese; Huan Zhang; Jun Tao; Robert J. Nemiroff
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Paper Abstract

Effective techniques for organizing and visualizing large image collections are in growing demand as visual search gets increasingly popular. Targeting an online astronomy archive with thousands of images, we present our solution for image search and clustering based on the evaluation image similarity using both visual and textual information. To lay out images, we introduce iMap, a treemap-based representation for visualizing and navigating image search and clustering results. iMap not only makes effective use of available display area to arrange images but also maintains stable update when images are inserted or removed during the query. We also develop an embedded visualization that integrates image tags for in-place search refinement. We show the effectiveness of our approach by demonstrating experimental results and conducting a comparative user study.

Paper Details

Date Published: 4 February 2013
PDF: 14 pages
Proc. SPIE 8654, Visualization and Data Analysis 2013, 86540K (4 February 2013); doi: 10.1117/12.999313
Show Author Affiliations
Chaoli Wang, Michigan Technological Univ. (United States)
John P. Reese, Michigan Technological Univ. (United States)
Huan Zhang, Michigan Technological Univ. (United States)
Jun Tao, Michigan Technological Univ. (United States)
Robert J. Nemiroff, Michigan Technological Univ. (United States)


Published in SPIE Proceedings Vol. 8654:
Visualization and Data Analysis 2013
Pak Chung Wong; David L. Kao; Ming C. Hao; Chaomei Chen, Editor(s)

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