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Faceted Metadata for Image Search and Browsing

Resource type
Authors/contributors
Title
Faceted Metadata for Image Search and Browsing
Abstract
There are currently two dominant interface types for searching and browsing large image collections: keyword-based search, and searching by overall similarity to sample images. We present an alternative based on enabling users to navigate along conceptual dimensions that describe the images. The interface makes use of hierarchical faceted metadata and dynamically generated query previews. A usability study, in which 32 art history students explored a collection of 35,000 fine arts images, compares this approach to a standard image search interface. Despite the unfamiliarity and power of the interface (attributes that often lead to rejection of new search interfaces), the study results show that 90% of the participants preferred the metadata approach overall, 97% said that it helped them learn more about the collection, 75% found it more flexible, and 72% found it easier to use than a standard baseline system. These results indicate that a category-based approach is a successful way to provide access to image collections.
Date
2003
Proceedings Title
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Place
New York, NY, USA
Publisher
ACM
Pages
401–408
Series
CHI '03
Language
en
DOI
10.1145/642611.642681
ISBN
978-1-58113-630-2
Accessed
2018-08-09T19:17:02Z
Library Catalog
ACM Digital Library
Citation
Yee, K.-P., Swearingen, K., Li, K., & Hearst, M. (2003). Faceted Metadata for Image Search and Browsing. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (pp. 401–408). New York, NY, USA: ACM. https://doi.org/10.1145/642611.642681
Field of study
Contribution