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. This paper presents an alternative in which users are able to navigate explicitly along conceptual dimensions that describe the images. The interface makes use of hierarchical faceted metadata and dynamically generated query previews. A usability study, conducted with 32 art history students exploring 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 which often lead to rejection of new search interfaces), the study results show that 90% of the participants preferred the meta-data 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 that a category-based approach is a successful way to provide access to image collections.

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The Effectiveness of Latent Semantic Analysis for Building Up a Bottom-up Taxonomy from Folksonomy Tags

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