Assessing Subject Metadata for Images
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This document summarizes the findings of assessing subject metadata for images in a library collection. It found that primary terms describing what an image depicts yielded the highest search utility and retrieval rates. Secondary and tertiary terms about what images were about or how they communicate were used less often. Descriptive non-subject terms worked best for 3D objects and abstract works when subject terms were less applicable. Overall, existing subject metadata only literally matched users' search terms 8.5% of the time, highlighting the challenges of subjective image interpretation. Primary terms provided the most consistent and useful access points for retrieving images.
Assessing Subject Metadata for Images
- 1. Assessing Subject Metadata for Images Hannah Marie Marshall, hmm88@cornell.edu Metadata Librarian for Image Collections Cornell University Library ARLIS/NA+VRA 2016 March 11, 2016 Seattle, Washington
- 2. Background
- 3. Assessment Goals • Determine retrieval rates • Determine the search utility • Primary Terms • “What is the image of?” • Secondary Terms • “What is the image about?” • Tertiary Terms • “How does the image communicate to the viewer?”
- 4. Challenges of subject analysis for images • "Image indexing is a complex socio-cognitive process that involves processing sensory input through classifying, abstracting, and mapping sensory data into concepts and entities often expressed through socially-defined and culturally-justified linguistic labels and identifiers" (Heidorn, 1999) • "Concept-based indexing has the advantage of providing higher-level analysis of the image content but is expensive to implement and suffers from a lack of inter-indexer consistency due to the subjective nature of image interpretation" (Chen, Rasmussen, 1999)
- 5. Findings – types of terms Search Utility • Primary Terms • “What is the image of?” • Secondary Terms • “What is the image about?” • Tertiary Terms • “How does the image communicate to the viewer?” • Non-subject Terms • Descriptive terms that don’t address the subject matter of the work (i.e. worktype, materials/techniques, style/period) 64% 34% 12% 13% 19% 16% 5% 37% EXISTING DATA USERS TYPES OF TERMS Primary Terms Secondary Terms Tertiary Terms Non-Subject Terms
- 6. Findings – types of terms Search Utility • Higher levels of correspondence for images of two-dimensional works • Higher retrieval rates • Higher search utility • Users were 2.5 times more likely to use non-subject terms to describe and search for images of three-dimensional works (and non-representational/abstract works) • Pottery, jewelry, sculpture 71.70% 45.30% 0 47.20% 26.40% 15.30% 16% 0 5% 8.20% 13% 19% 0 32% 16.80% 0% 19.70% 0 15.80% 48.60% EXISTING DATA USERS EXISTING DATA USERS 2D WORKS VS. 3D WORKS Primary Terms Secondary Terms Tertiary Terms Non-Subject Terms
- 7. Findings – types of terms Search Utility • Users were 2.5 times more likely to use non-subject terms to describe and search for images of three-dimensional works (and non-representational/abstract works) • Pottery, jewelry, sculpture 0% 10% 20% 30% 40% 50% 60% Worktype Style/Period Materials/Techniques Culture Most common types of non- subject access points
- 8. Findings – literal terms Retrieval Rates • Literal matches = successful image retrieval • Non-matches = unsuccessful image retrieval • Successful retrieval = 8.5% • Unsuccessful retrieval = 91.5% Correspondence between existing metadata and users’ search terms Non-matches Literal Matches
- 9. Findings – literal terms Retrieval Rates • Of that 8.5%... • Primary Terms (75%) • “What is the image of?” • Secondary Terms (3%) • “What is the image about?” • Tertiary Terms (16%) • “How does the image communicate to the viewer?” • Non-subject Terms (6%) • Other descriptive metadata that does not address subject meaning (i.e. materials and techniques) Corresponding literal terms broken down by type Primary Terms Secondary Terms Tertiary Terms Non-Subject Terms
- 10. Conclusions • Primary terms yield the greatest search utility and higher levels of successful image retrieval. • High numbers of non-subject terms applied to images of three-dimensional and non- representational works suggest that subject metadata is a weak access point for them
- 11. Thank you!