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| Content Provider | ACM Digital Library |
|---|---|
| Author | Li, Kevin Swearingen, Kirsten Yee, Ka-Ping Hearst, Marti |
| 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. |
| Starting Page | 401 |
| Ending Page | 408 |
| Page Count | 8 |
| File Format | |
| ISBN | 1581136307 |
| DOI | 10.1145/642611.642681 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2003-04-05 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Subject Keyword | Faceted metadata Image search interfaces |
| Content Type | Text |
| Resource Type | Article |
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|---|---|---|---|
| 1 | Ministry of Education (GoI), Department of Higher Education |
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