"Tagging itself is challenging as it involves converting an image's pixels to descriptive words," said James Wang, lead researcher and associate professor of information sciences and technology at Penn State.
"But what is novel with the Tagging over Time technology is that the system adapts as people's preferences for images and words change."
The system can accommodate evolving vocabulary and interpretations to images that people have uploaded and are uploading to systems such as Yahoo's Flickr.
This allows the system's vocabulary to grow, replacing old tags with more relevant and more specific new tags, Wang explained.
In tests, the Tagging over Time technology correctly annotated four out of every 10 images, a significant improvement over the researchers' earlier annotation system known as Automatic Linguistic Indexing of Pictures-Real Time.
In the previous system, pixel content of images was analysed to suggest annotations.
In the new software, researchers have added a machine-learning component that enables the computer to learn from user interaction with photo-sharing systems.
"The bottom line is that the system makes it easier to find photographs and is able to improve its performance by itself as time passes," added Ritendra Datta, a graduate student in computer science working with Wang.
"The advancement means time savings for consumers as well as improved searching and referral capabilities."
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