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Title: MultiFacet: A Faceted Interface for Browsing Large Multimedia Collections

Faceted browsing is a common technique for exploring collections where the data can be grouped into a number of pre-defined categories, most often generated from textual metadata. Historically, faceted browsing has been applied to a single data type such as text or image data. However, typical collections contain multiple data types, such as information from web pages that contain text, images, and video. Additionally, when browsing a collection of images and video, facets are often created based on the metadata which may be incomplete, inaccurate, or missing altogether instead of the actual visual content contained within those images and video. In this work we address these limitations by presenting MultiFacet, a faceted browsing interface that supports multiple data types. MultiFacet constructs facets for images and video in a collection from the visual content using computer vision techniques. These visual facets can then be browsed in conjunction with text facets within a single interface to reveal relationships and phenomena within multimedia collections. Additionally, we present a use case based on real-world data, demonstrating the utility of this approach towards browsing a large multimedia data collection.
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Conference: IEEE International Symposium on Multimedia (ISM2013), December 9-11, 2013, Anaheim, California, 347 - 350
IEEE, Piscataway, NJ, United States(US).
Research Org:
Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
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Country of Publication:
United States
Visual Analytics; content-based image retrieval; image classification; faceted search; visualization; multimedia