Can Global Visual Features Improve Tag Recommendation for Image Annotation?

Mathias Lux, Arthur Pitman and Oge Marques
Additional contact information
Mathias Lux: Institute for Information Technology, Klagenfurt University, Universitaetsstr, 65-67, 9020 Klagenfurt, Austria
Arthur Pitman: Institute for Applied Informatics, Klagenfurt University, Universitaetsstr, 65-67, 9020 Klagenfurt, Austria
Oge Marques: Department of Computer & Electrical Engineering and Computer Science, Florida Atlantic University, 777 Glades Rd, Boca Raton, FL 33431, USA

Future Internet, 2010, vol. 2, issue 3, 1-22

Abstract: Recent advances in the fields of digital photography, networking and computing, have made it easier than ever for users to store and share photographs. However without sufficient metadata, e.g., in the form of tags , photos are difficult to find and organize. In this paper, we describe a system that recommends tags for image annotation. We postulate that the use of low-level global visual features can improve the quality of the tag recommendation process when compared to a baseline statistical method based on tag co-occurrence. We present results from experiments conducted using photos and metadata sourced from the Flickr photo website that suggest that the use of visual features improves the mean average precision (MAP) of the system and increases the system's ability to suggest different tags, therefore justifying the associated increase in complexity.

Keywords: image retrieval; multimedia; metadata; folksonomies; tagging; image annotation; tag recommendation; visual information retrieval (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2010
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1999-5903/2/3/341/pdf (application/pdf)
https://www.mdpi.com/1999-5903/2/3/341/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jftint:v:2:y:2010:i:3:p:341-362:d:9420

Access Statistics for this article

Future Internet is currently edited by Ms. Grace You

More articles in Future Internet from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().