Utilising semantic technologies for intelligent indexing and retrieval of digital images
The proliferation of digital media has led to a huge interest in classifying and indexing media objects for generic search and usage. In particular, we are witnessing a colossal growth in digital image repositories that are difficult to navigate using free-text search mechanisms, which often return...
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| Veröffentlicht in: | Computing Jg. 96; H. 7; S. 651 - 668 |
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01.07.2014
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| ISSN: | 0010-485X, 1436-5057 |
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| Abstract | The proliferation of digital media has led to a huge interest in classifying and indexing media objects for generic search and usage. In particular, we are witnessing a colossal growth in digital image repositories that are difficult to navigate using 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 is designed to satisfy the requirements of the commercial image collections market in terms of both accuracy and efficiency of the retrieval process. Our search engine 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. We also show how our well-analysed and designed domain ontology contributes to the implicit expansion of user queries as well as the exploitation of lexical databases for explicit semantic-based query expansion. |
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| AbstractList | The proliferation of digital media has led to a huge interest in classifying and indexing media objects for generic search and usage. In particular, we are witnessing a colossal growth in digital image repositories that are difficult to navigate using 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 is designed to satisfy the requirements of the commercial image collections market in terms of both accuracy and efficiency of the retrieval process. Our search engine 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. We also show how our well-analysed and designed domain ontology contributes to the implicit expansion of user queries as well as the exploitation of lexical databases for explicit semantic-based query expansion. Issue Title: Special Issue on MobiWIS 2012 The proliferation of digital media has led to a huge interest in classifying and indexing media objects for generic search and usage. In particular, we are witnessing a colossal growth in digital image repositories that are difficult to navigate using 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 is designed to satisfy the requirements of the commercial image collections market in terms of both accuracy and efficiency of the retrieval process. Our search engine 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. We also show how our well-analysed and designed domain ontology contributes to the implicit expansion of user queries as well as the exploitation of lexical databases for explicit semantic-based query expansion.[PUBLICATION ABSTRACT] |
| Author | Schaefer, Gerald Thakker, Dhavalkumar Osman, Taha |
| Author_xml | – sequence: 1 givenname: Taha surname: Osman fullname: Osman, Taha organization: School of Computing and Informatics, Nottingham Trent University – sequence: 2 givenname: Dhavalkumar surname: Thakker fullname: Thakker, Dhavalkumar organization: School of Computing, University of Leeds – sequence: 3 givenname: Gerald surname: Schaefer fullname: Schaefer, Gerald email: gerald.schaefer@ieee.org organization: Department of Computer Science, Loughborough University |
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| Cites_doi | 10.1109/ICSC.2007.31 10.1145/1180639.1180672 10.1109/34.895972 10.1007/s00778-003-0102-4 10.1007/11919629_77 10.7551/mitpress/7287.001.0001 10.1016/j.ipm.2003.11.001 10.1145/945645.945664 10.1007/978-3-540-85563-7_26 10.1109/ICME.2006.262892 10.1145/1013367.1013381 10.1109/ICWS.2006.118 10.1109/MIS.2006.62 10.1145/642611.642681 10.1016/j.ipm.2006.09.003 10.1145/860435.860459 10.1007/s11263-006-8614-1 10.1016/j.patcog.2006.04.045 10.1016/S1071-5819(02)00127-1 10.1145/1348246.1348248 10.1109/MCI.2006.1626490 10.1117/12.647755 |
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| References | GaihuaFOntology-based spatial query expansion in information retrievalLect Notes Comput Sci20053761/200514661482 Hare JS, Lewis PH, Enser PGB, Sandom CJ (2006) Mind the gap: another look at the problem of the semantic gap in image retrieval. In: Proceedings multimedia content analysis, management and retrieval 2006, 607309 – 1. http://eprints.soton.ac.uk/261887 FellbauCWordNet—an electronic lexical database1998CambridgeThe MIT press Fuji A, Ishikawa T (2005) Toward the automatic compilation of multimedia encyclopaedias: association images with term descriptions on the web. In: Proceedings of the 2005 international conference on web intelligence, pp 536–542 ShadboltNBerners-LeeTHallWThe semantic web revisitedIEEE Intell Syst20062139610110.1109/MIS.2006.62 DattaRJoshiDLiJWangJZImage retrieval: ideas, influences, and trends of the new ageACM Comput Surv200840216010.1145/1348246.1348248 MaedcheAMotikBStojanovicLManaging multiple and distributed ontologies on the Semantic WebInt J Very Large Data Bases2003124286302 Frkovic F, Podobnik V, Trzec K, Jezic, G (2008) Agent-based user personalization using context-aware semantic reasoning. In: 12th international conference on knowledge-based intelligent information and engineering systems, pp 166–73 SmeuldersAWorringMSantiniSGuptaAJainRContent-based image retrieval at the end of the early yearsIEEE Trans Pattern Anal Mach Intell2000221212491380 Ching-man Au Y, Gibbins N, Shadbolt N (2008) A k-nearest-neighbour method for classifying web search results with data in folksonomies. Web intelligence and intelligent agent technology, 2008. International conference on WI-IAT’08. IEEE/WIC/ACM , vol 1. IEEE BhogalJMacfarlaneASmithPA review of ontology based query expansionInform Process Manag Int J200743486688610.1016/j.ipm.2006.09.003 Carroll J et al. (2004) Jena: implementing the semantic web recommendations. In: Proceedings of the 13th international world wide web conference. New York, USA, pp 74–83 Yee K et al. (2003) Facated meta data for image search and browsing. In: Proceedings of the SIGCHI conference on human factors in computing systems, pp 401–408 Lam T, Singh R (2006) Semantically relevant image retrieval by combining image and linguistic analysis. In: Proceedings of the international symposium on visual computing, pp 1686–1695 Osman T, Thakker D, Al-Dabass D (2006) Semantic-driven matchmaking of web services using case-based reasoning. In: IEEE international conference on web services (ICWS’06), September 2006, pp 29–36 WuKYapKFuzzy SVM for content-based image retrieval: a pseudo-label support vector machine frameworkIEEE Comput Intell Mag200612101610.1109/MCI.2006.1626490May 2006 Haubold A, Natsev A, Naphade M (2006) Semantic multimedia retrieval using lexical query expansion and model-based reranking. In: IEEE International Conference on Multimedia and Expo, pp 1761–1764 Park Y, Li Y (2007) Semantic analysis for topical segmentation of videos. International conference on semantic computing, pp 161–168 LeeCGeunbaeGProbabilistic information retrieval model for a dependency structured indexing systemInt J Inform Process Manage200541216117510.1016/j.ipm.2003.11.0011101.68540 VogelJSchieleBSemantic modeling of natural scenes for content-based image retrievalInt J Comput Vis200772213315710.1007/s11263-006-8614-1(2007) Hollink L et al. (2003) Semantic annotation of image collections. In: Workshop on knowledge markup and semantic annotation Buitelaar P, Ramaka S (2005) Unsupervised ontology-based semantic tagging for knowledge markup. In: Proceedings of the workshop on learning in web search at the international conference on machine learning Wang H, Liu, Chia L (2006) Does ontology help in image retrieval? A comparison between keyword, text ontology and multi-modality ontology approaches. In: Proceedings of the 14th annual ACM international conference on multimedia, pp 109–112 Jeon J et al. (2003) Automatic image annotation and retrieval using cross-media relevance models. In: Proceedings of the 26th annual international ACM SIGIR conference on research and development in information retrieval Rector A (2003) Modularisation of domain ontologies implemented in description logics and related formalisms including OWL. In: Proceedings of the 2nd international conference on knowledge capture, pp 121–128 YingLA survey of content-based image retrieval with high-level semanticsPattern Recogn200740126228210.1016/j.patcog.2006.04.0451103.68503 W3C Corporation [online] (2004) OWL web ontology language overview. http://www.w3.org/TR/owl-features NoyNTuSThe evolution of Protégé-2000: an environment for knowledge-based systems developmentIn J Human-Comput Stud20035818912310.1016/S1071-5819(02)00127-1 C Fellbau (350_CR6) 1998 J Bhogal (350_CR2) 2007; 43 N Shadbolt (350_CR21) 2006; 21 350_CR25 350_CR24 350_CR27 N Noy (350_CR17) 2003; 58 A Smeulders (350_CR22) 2000; 22 350_CR10 C Lee (350_CR15) 2005; 41 350_CR19 L Ying (350_CR28) 2007; 40 J Vogel (350_CR23) 2007; 72 350_CR8 350_CR7 350_CR12 350_CR11 350_CR14 K Wu (350_CR26) 2006; 1 350_CR4 F Gaihua (350_CR9) 2005; 3761/2005 350_CR13 350_CR3 350_CR16 350_CR1 350_CR18 R Datta (350_CR5) 2008; 40 350_CR20 |
| References_xml | – reference: SmeuldersAWorringMSantiniSGuptaAJainRContent-based image retrieval at the end of the early yearsIEEE Trans Pattern Anal Mach Intell2000221212491380 – reference: Ching-man Au Y, Gibbins N, Shadbolt N (2008) A k-nearest-neighbour method for classifying web search results with data in folksonomies. Web intelligence and intelligent agent technology, 2008. International conference on WI-IAT’08. IEEE/WIC/ACM , vol 1. IEEE – reference: Yee K et al. (2003) Facated meta data for image search and browsing. In: Proceedings of the SIGCHI conference on human factors in computing systems, pp 401–408 – reference: Frkovic F, Podobnik V, Trzec K, Jezic, G (2008) Agent-based user personalization using context-aware semantic reasoning. In: 12th international conference on knowledge-based intelligent information and engineering systems, pp 166–73 – reference: MaedcheAMotikBStojanovicLManaging multiple and distributed ontologies on the Semantic WebInt J Very Large Data Bases2003124286302 – reference: GaihuaFOntology-based spatial query expansion in information retrievalLect Notes Comput Sci20053761/200514661482 – reference: Wang H, Liu, Chia L (2006) Does ontology help in image retrieval? A comparison between keyword, text ontology and multi-modality ontology approaches. In: Proceedings of the 14th annual ACM international conference on multimedia, pp 109–112 – reference: WuKYapKFuzzy SVM for content-based image retrieval: a pseudo-label support vector machine frameworkIEEE Comput Intell Mag200612101610.1109/MCI.2006.1626490May 2006 – reference: Jeon J et al. (2003) Automatic image annotation and retrieval using cross-media relevance models. In: Proceedings of the 26th annual international ACM SIGIR conference on research and development in information retrieval – reference: YingLA survey of content-based image retrieval with high-level semanticsPattern Recogn200740126228210.1016/j.patcog.2006.04.0451103.68503 – reference: Hare JS, Lewis PH, Enser PGB, Sandom CJ (2006) Mind the gap: another look at the problem of the semantic gap in image retrieval. In: Proceedings multimedia content analysis, management and retrieval 2006, 607309 – 1. http://eprints.soton.ac.uk/261887/ – reference: Haubold A, Natsev A, Naphade M (2006) Semantic multimedia retrieval using lexical query expansion and model-based reranking. In: IEEE International Conference on Multimedia and Expo, pp 1761–1764 – reference: LeeCGeunbaeGProbabilistic information retrieval model for a dependency structured indexing systemInt J Inform Process Manage200541216117510.1016/j.ipm.2003.11.0011101.68540 – reference: Hollink L et al. (2003) Semantic annotation of image collections. In: Workshop on knowledge markup and semantic annotation – reference: Osman T, Thakker D, Al-Dabass D (2006) Semantic-driven matchmaking of web services using case-based reasoning. In: IEEE international conference on web services (ICWS’06), September 2006, pp 29–36 – reference: DattaRJoshiDLiJWangJZImage retrieval: ideas, influences, and trends of the new ageACM Comput Surv200840216010.1145/1348246.1348248 – reference: Carroll J et al. (2004) Jena: implementing the semantic web recommendations. In: Proceedings of the 13th international world wide web conference. New York, USA, pp 74–83 – reference: FellbauCWordNet—an electronic lexical database1998CambridgeThe MIT press – reference: Rector A (2003) Modularisation of domain ontologies implemented in description logics and related formalisms including OWL. 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In: Proceedings of the international symposium on visual computing, pp 1686–1695 – reference: NoyNTuSThe evolution of Protégé-2000: an environment for knowledge-based systems developmentIn J Human-Comput Stud20035818912310.1016/S1071-5819(02)00127-1 – reference: W3C Corporation [online] (2004) OWL web ontology language overview. http://www.w3.org/TR/owl-features – reference: ShadboltNBerners-LeeTHallWThe semantic web revisitedIEEE Intell Syst20062139610110.1109/MIS.2006.62 – reference: Park Y, Li Y (2007) Semantic analysis for topical segmentation of videos. 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