An integrative semantic framework for image annotation and retrieval

Taha Osman; Dhavalkumar Thakker; Gerald Schaefer; Phil Lakin

doi:10.1109/WI.2007.17

An integrative semantic framework for image annotation and retrieval

Taha Osman^*
, Dhavalkumar Thakker
, Gerald Schaefer
, Phil Lakin

^*Corresponding author for this work

Research output: Chapter in Book/Published conference output › Conference publication

24 Citations (Scopus)

29 Downloads (Pure)

Abstract

Most public image retrieval engines utilise free-text search mechanisms, which often return inaccurate matches as they in principle rely on statistical analysis of query keyword recurrence in the image annotation or surrounding text. In this paper we present a semantically-enabled image annotation and retrieval engine that relies on methodically structured ontologies for image annotation, thus allowing for more intelligent reasoning about the image content and subsequently obtaining a more accurate set of results and a richer set of alternatives matchmaking the original query. Our semantic retrieval technology is designed to satisfy the requirements of the commercial image collections market in terms of both accuracy and efficiency of the retrieval process. We also present our efforts in further improving the recall of our retrieval technology by deploying an efficient query expansion technique.

Original language	English
Title of host publication	Proceedings of the IEEE/WIC/ACM International Conference on Web Intelligence, WI 2007
Pages	366-373
Number of pages	8
DOIs	https://doi.org/10.1109/WI.2007.17
Publication status	Published - 7 Jan 2008
Event	IEEE/WIC/ACM International Conference on Web Intelligence, WI 2007 - Silicon Valley, CA, United Kingdom Duration: 2 Nov 2007 → 5 Nov 2007

Conference

Conference	IEEE/WIC/ACM International Conference on Web Intelligence, WI 2007
Country/Territory	United Kingdom
City	Silicon Valley, CA
Period	2/11/07 → 5/11/07

Bibliographical note

© 2007 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

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10.1109/WI.2007.17

A Semantically Enriched Competency Management System to Support the Analysis of a Web-based Research Network
© 2007 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Accepted author manuscript, 885 KB

http://wwwusers.di.uniroma1.it/~velardi/WI07_sub.pdf

Cite this

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title = "An integrative semantic framework for image annotation and retrieval",

abstract = "Most public image retrieval engines utilise free-text search mechanisms, which often return inaccurate matches as they in principle rely on statistical analysis of query keyword recurrence in the image annotation or surrounding text. In this paper we present a semantically-enabled image annotation and retrieval engine that relies on methodically structured ontologies for image annotation, thus allowing for more intelligent reasoning about the image content and subsequently obtaining a more accurate set of results and a richer set of alternatives matchmaking the original query. Our semantic retrieval technology is designed to satisfy the requirements of the commercial image collections market in terms of both accuracy and efficiency of the retrieval process. We also present our efforts in further improving the recall of our retrieval technology by deploying an efficient query expansion technique.",

author = "Taha Osman and Dhavalkumar Thakker and Gerald Schaefer and Phil Lakin",

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Osman, T, Thakker, D, Schaefer, G & Lakin, P 2008, An integrative semantic framework for image annotation and retrieval. in Proceedings of the IEEE/WIC/ACM International Conference on Web Intelligence, WI 2007., 4427118, pp. 366-373, IEEE/WIC/ACM International Conference on Web Intelligence, WI 2007, Silicon Valley, CA, United Kingdom, 2/11/07. https://doi.org/10.1109/WI.2007.17

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N1 - © 2007 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

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AB - Most public image retrieval engines utilise free-text search mechanisms, which often return inaccurate matches as they in principle rely on statistical analysis of query keyword recurrence in the image annotation or surrounding text. In this paper we present a semantically-enabled image annotation and retrieval engine that relies on methodically structured ontologies for image annotation, thus allowing for more intelligent reasoning about the image content and subsequently obtaining a more accurate set of results and a richer set of alternatives matchmaking the original query. Our semantic retrieval technology is designed to satisfy the requirements of the commercial image collections market in terms of both accuracy and efficiency of the retrieval process. We also present our efforts in further improving the recall of our retrieval technology by deploying an efficient query expansion technique.

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An integrative semantic framework for image annotation and retrieval

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