Automatic Caption Localization for Photographs on World Wide Web Pages

Bibliographic description
ROWE, Neil C.; FEW, Brian. Automatic Caption Localization for Photographs on World Wide Web Pages [on line]. Monterey: Department of Computer Science, U. S. Naval Postgraduate School, 1998. Available on: http://www.nps.navy.mil/Content/CS/ncrowe/marie/webpics.html"

Dublin Core
Title : Automatic Caption Localization for Photographs on World Wide Web Pages
Creator : Neil C. Rowe, Brian Frew
Subject : photograph retrieval / Photograph indexing / World Wide Web
Description : "Pictures, especially photographs, are one of the most valuable resources available on the Internet through the popular World Wide Web. Unlike text, most photographs are valuable primary sources of real-world data. Unlike conventional copy technology, photographs on the Web maintain their quality under transmission. Interest has increased recently in multimedia technology as its speed has improved by hardware and software refinements; photographs also benefit from these advances. This has meant that multimedia resources on the Web have grown quickly. For these reasons, indexing and retrieval of photographs is becoming increasingly critical."
Publisher : Department of Computer Science, U. S. Naval Postgraduate School, Monterey
Date : 1998
Type : Article
Format : HTML
Identifier : http://www.nps.navy.mil/Content/CS/ncrowe/marie/webpics.html
Source: http://www.nps.edu/
Language : En
Relation : -
Coverage : USA
Rights : U. S. Naval Postgraduate School

Abstract
"A variety of software tools index text of the World Wide Web, but little attention has been paid to the many photographs. We explore the indirect method of locating for indexing the likely explicit and implicit captions of photographs. We use multimodal clues including the specific words used, the syntax, the surrounding layout of the Web page, and the general appearance of the associated image. Our MARIE-3 system thus avoids full image processing and full natural-language processing, but shows a surprising degree of success. Experiments with a semi-random set of Web pages showed 41% recall with 41% precision for the task of distinguishing captions from other text, and 70% recall with 30% precision. This is much better than chance since actual captions were only 1.4% of the text on pages with photographs."