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Proceedings Paper

iGraph: a graph-based technique for visual analytics of image and text collections
Author(s): Yi Gu; Chaoli Wang; Jun Ma; Robert J. Nemiroff; David L. Kao
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Paper Abstract

In our daily lives, images and texts are among the most commonly found data which we need to handle. We present iGraph, a graph-based approach for visual analytics of large image and text collections. Given such a collection, we compute the similarity between images, the distance between texts, and the connection between image and text to construct iGraph, a compound graph representation which encodes the underlying relationships among these images and texts. To enable effective visual navigation and comprehension of iGraph with tens of thousands of nodes and hundreds of millions of edges, we present a progressive solution that offers collection overview, node comparison, and visual recommendation. Our solution not only allows users to explore the entire collection with representative images and keywords, but also supports detailed comparison for understanding and intuitive guidance for navigation. For performance speedup, multiple GPUs and CPUs are utilized for processing and visualization in parallel. We experiment with two image and text collections and leverage a cluster driving a display wall of nearly 50 million pixels. We show the effectiveness of our approach by demonstrating experimental results and conducting a user study.

Paper Details

Date Published: 8 February 2015
PDF: 15 pages
Proc. SPIE 9397, Visualization and Data Analysis 2015, 939708 (8 February 2015); doi: 10.1117/12.2074198
Show Author Affiliations
Yi Gu, Univ. of Notre Dame (United States)
Chaoli Wang, Univ. of Notre Dame (United States)
Jun Ma, Michigan Technological Univ. (United States)
Robert J. Nemiroff, Michigan Technological Univ. (United States)
David L. Kao, NASA Ames Research Ctr. (United States)

Published in SPIE Proceedings Vol. 9397:
Visualization and Data Analysis 2015
David L. Kao; Ming C. Hao; Mark A. Livingston; Thomas Wischgoll, Editor(s)

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