Faceted metadata for image search and browsing

TLDR

Current image search interfaces are mainly keyword‑based or similarity‑based. The study proposes a new interface that lets users navigate images along conceptual dimensions. The interface employs hierarchical faceted metadata and dynamic query previews, and its usability was evaluated with 32 art history students on a 35,000‑image fine‑arts collection against a standard search baseline. Results showed 90% preferred the metadata interface, 97% felt it enhanced learning, 75% found it more flexible, and 72% found it easier to use, confirming that a category‑based approach effectively accesses image collections.

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.