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INTELEK Repository
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Online Access
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2024-08-27 13:49:49
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12745
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[1] R. Datta, D. Joshi, J. Li, and J.Z Wang, Image Retrieval: Ideas, Influences, and Trends of the New Age, Journal of ACM Computing Surveys, 40 (2008), no. 2, 1- 5. http://dx.doi.org/10.1145/1348246.1348248 [2] M. Jain, and S.K. Singh, An Experimental Study on Content Based Image Retrieval Based On Number of Clusters Using Hierarchical Clustering Algorithm, International Journal of Signal Processing, Image Processing and Pattern Recognition, 7 (2014), no. 4, 105-114. http://dx.doi.org/10.14257/ijsip.2014.7.4.10 [3] E.M. Voorhees, and D. Harman, Overview of the 8th Text Retrieval Conference, Proceedings of the 8th Text Retrieval Conference, NIST, Gaitherburg, MD, (1999), 1–24. [4] S. Gauch, J.M. Madrid, S. Induri, D. Ravindran, and S. Chadlavada, Key concept: A Conceptual Search Engine, Technical Report TR-8646-37, University of Kansas, (2003). [5] W. Guo, and M.T. Diab, Improving Lexical Semantics for Sentential Semantics: Modeling Selectional Preference and Similar Words In A Latent Variable Model, Proceedings of NAACL, Atlanta, Georgia, USA, (2013). [6] X. Yi, and J. Allan, A Comparative Study of Utilizing Topic Models for Information Retrieval, Proceedings of the 31st European Conference on Information Retrieval Research, Toulouse, France, (2009), 29–41. http://dx.doi.org/10.1007/978-3-642-00958-7_6 [7] S.A. Fadzli, and R. Setchi, Concept-Based Indexing of Annotated Images Using Semantic DNA, Journal of Engineering Application of Artificial Intelligence, 25 (2012), no. 8, 1644-1655. http://dx.doi.org/10.1016/j.engappai.2012.02.005 [8] M. Montague, and J.A. Aslam, Metasearch Consistency, Proceedings of the 24th Annual International ACM Special Interest Group on Information Retrieval Conference on Research and Development in Information Retrieval, New Orleans, Louisiana, USA, (2001), 386-387. http://dx.doi.org/10.1145/383952.384030 [9] L. Popa, Y. Velegrakis, R.J. Miller, M.A Hern´Andez,. and R. Fagin, Translating Web Data, Proceedings of the 28th International Conference on Very Large Databases, Hong Kong, China, (2002), 598-609. http://dx.doi.org/10.1016/b978-155860869-6/50059-7 [10] J. Bleiholder, and F. Naumann, Data Fusion, Journal of ACM Computing Surveys (CSUR), 41 (2008), no. 1, 1-41. http://dx.doi.org/10.1145/1456650.1456651 [11] J. H. Lee, Combining Multiple Evidence from Different Properties of Weighting Schemes, Proceedings of the 18th ACM Conference on Research and Development in Information Retrieval, Seattle, Washington, USA, (1995), 180– 188. http://dx.doi.org/10.1145/215206.215358 [12] J. H. Lee, Analysis of Multiple Evidence Combination, Proceeding of the 20th ACM International Conference on Research and Development in Information Retrieval, Philadelphia, PA , USA, (1997), 267-276. http://dx.doi.org/10.1145/258525.258587 [13] W. Bruce Croft, Combining Approaches to Information Retrieval, Journal of Advances in Information Retrieval: Recent Research from the Center for Intelligent Information Retrieval, Kluwer Academic Publishers, (2000) 1-36. [14] A. Yavlinsky, M. J. Pickering, D. Heesch, and S. Ruger, A Comparative Study of Evidence Combination Strategies, Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing, 3 (2004), 1023-1040. Montreal, Quebec, Canada. http://dx.doi.org/10.1109/icassp.2004.1326726 [15] J. A. Shaw, and E. A. Fox, Combination of Multiple Searches, Proceedings of The 3rd Text Retrieval Conference (TREC 3), Gaithersburg, Maryland, USA, 105-108, 1994. [16] C. C. Vogt, and G. W. Cottrell, Fusion via a Linear Combination of Scores, Journal of Information Retrieval, 1 (1999), no. 3, 151-173. http://dx.doi.org/10.1023/a:1009980820262 [17] VisConPro, fotoLIBRA, The Image Warehouse [Online], Available at: http://www.fotolibra.com [Accessed 21/01/2015]. [18] C. Xu, Y. Bai, J. Bian, B. Gao, G. Wang, X. Liu, and T.Y. Liu, RC-NET: A general framework for incorporating knowledge into word representations, Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management, New York, USA, (2014), 1219-1228. http://dx.doi.org/10.1145/2661829.2662038
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https://intelek.unisza.edu.my/intelek/pages/view.php?ref=12745
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12745 https://intelek.unisza.edu.my/intelek/pages/view.php?ref=12745 https://intelek.unisza.edu.my/intelek/pages/search.php?search=!collection407072 Restricted Document Article Journal application/pdf Adobe Acrobat Pro DC 20 Paper Capture Plug-in with ClearScan 16 1.6 Rokawa 2024-08-27 13:49:49 7052-01-FH02-FIK-16-05223.pdf UniSZA Private Access Fusion Model for Ontology Based Image Retrieval Applied Mathematical Sciences Data fusion is the process of combining multiple sources of information to produce better results compared to using the source individually. This paper applies the idea of data fusion to semantic image retrieval, which combines the ranking scores between ontology based and keyword based semantic image retrieval model. Although the evaluation shows that the overall performance of the ontology based model is higher than that of the keyword based model, the results analysis reveals that the performance of ontology based model is in direct relation with the implicit information relies within the query and annotation text. If the annotation contains less meaningful information, the ontology based method performs very poorly, thus affecting the relevancy of semantic chromosomes. This further affects the performance of the similarity measure and the quality of the retrieval results. As a result, user queries return fewer results than expected, as they get much lower similarity value than they should. Whereas, keyword-based search would perform better in these situations. To deal with this drawback, this paper proposes to combine the results coming from the proposed ontology-based retrieval model and the result returned by traditional keyword-based model. The combined model is evaluated using both traditional IR measures. 9 130 Hikari Ltd. Hikari Ltd. 6461-6475 [1] R. Datta, D. Joshi, J. Li, and J.Z Wang, Image Retrieval: Ideas, Influences, and Trends of the New Age, Journal of ACM Computing Surveys, 40 (2008), no. 2, 1- 5. http://dx.doi.org/10.1145/1348246.1348248 [2] M. Jain, and S.K. Singh, An Experimental Study on Content Based Image Retrieval Based On Number of Clusters Using Hierarchical Clustering Algorithm, International Journal of Signal Processing, Image Processing and Pattern Recognition, 7 (2014), no. 4, 105-114. http://dx.doi.org/10.14257/ijsip.2014.7.4.10 [3] E.M. Voorhees, and D. Harman, Overview of the 8th Text Retrieval Conference, Proceedings of the 8th Text Retrieval Conference, NIST, Gaitherburg, MD, (1999), 1–24. [4] S. Gauch, J.M. Madrid, S. Induri, D. Ravindran, and S. Chadlavada, Key concept: A Conceptual Search Engine, Technical Report TR-8646-37, University of Kansas, (2003). [5] W. Guo, and M.T. Diab, Improving Lexical Semantics for Sentential Semantics: Modeling Selectional Preference and Similar Words In A Latent Variable Model, Proceedings of NAACL, Atlanta, Georgia, USA, (2013). [6] X. Yi, and J. Allan, A Comparative Study of Utilizing Topic Models for Information Retrieval, Proceedings of the 31st European Conference on Information Retrieval Research, Toulouse, France, (2009), 29–41. http://dx.doi.org/10.1007/978-3-642-00958-7_6 [7] S.A. Fadzli, and R. Setchi, Concept-Based Indexing of Annotated Images Using Semantic DNA, Journal of Engineering Application of Artificial Intelligence, 25 (2012), no. 8, 1644-1655. http://dx.doi.org/10.1016/j.engappai.2012.02.005 [8] M. Montague, and J.A. Aslam, Metasearch Consistency, Proceedings of the 24th Annual International ACM Special Interest Group on Information Retrieval Conference on Research and Development in Information Retrieval, New Orleans, Louisiana, USA, (2001), 386-387. http://dx.doi.org/10.1145/383952.384030 [9] L. Popa, Y. Velegrakis, R.J. Miller, M.A Hern´Andez,. and R. Fagin, Translating Web Data, Proceedings of the 28th International Conference on Very Large Databases, Hong Kong, China, (2002), 598-609. http://dx.doi.org/10.1016/b978-155860869-6/50059-7 [10] J. Bleiholder, and F. Naumann, Data Fusion, Journal of ACM Computing Surveys (CSUR), 41 (2008), no. 1, 1-41. http://dx.doi.org/10.1145/1456650.1456651 [11] J. H. Lee, Combining Multiple Evidence from Different Properties of Weighting Schemes, Proceedings of the 18th ACM Conference on Research and Development in Information Retrieval, Seattle, Washington, USA, (1995), 180– 188. http://dx.doi.org/10.1145/215206.215358 [12] J. H. Lee, Analysis of Multiple Evidence Combination, Proceeding of the 20th ACM International Conference on Research and Development in Information Retrieval, Philadelphia, PA , USA, (1997), 267-276. http://dx.doi.org/10.1145/258525.258587 [13] W. Bruce Croft, Combining Approaches to Information Retrieval, Journal of Advances in Information Retrieval: Recent Research from the Center for Intelligent Information Retrieval, Kluwer Academic Publishers, (2000) 1-36. [14] A. Yavlinsky, M. J. Pickering, D. Heesch, and S. Ruger, A Comparative Study of Evidence Combination Strategies, Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing, 3 (2004), 1023-1040. Montreal, Quebec, Canada. http://dx.doi.org/10.1109/icassp.2004.1326726 [15] J. A. Shaw, and E. A. Fox, Combination of Multiple Searches, Proceedings of The 3rd Text Retrieval Conference (TREC 3), Gaithersburg, Maryland, USA, 105-108, 1994. [16] C. C. Vogt, and G. W. Cottrell, Fusion via a Linear Combination of Scores, Journal of Information Retrieval, 1 (1999), no. 3, 151-173. http://dx.doi.org/10.1023/a:1009980820262 [17] VisConPro, fotoLIBRA, The Image Warehouse [Online], Available at: http://www.fotolibra.com [Accessed 21/01/2015]. [18] C. Xu, Y. Bai, J. Bian, B. Gao, G. Wang, X. Liu, and T.Y. Liu, RC-NET: A general framework for incorporating knowledge into word representations, Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management, New York, USA, (2014), 1219-1228. http://dx.doi.org/10.1145/2661829.2662038
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| spellingShingle |
Fusion Model for Ontology Based Image Retrieval
|
| summary |
Data fusion is the process of combining multiple sources of information to produce better results compared to using the source individually. This paper applies the idea of data fusion to semantic image retrieval, which combines the ranking scores between ontology based and keyword based semantic image retrieval model. Although the evaluation shows that the overall performance of the ontology based model is higher than that of the keyword based model, the results analysis reveals that the performance of ontology based model is in direct relation with the implicit information relies within the query and annotation text. If the annotation contains less meaningful information, the ontology based method performs very poorly, thus affecting the relevancy of semantic chromosomes. This further affects the performance of the similarity measure and the quality of the retrieval results. As a result, user queries return fewer results than expected, as they get much lower similarity value than they should. Whereas, keyword-based search would perform better in these situations. To deal with this drawback, this paper proposes to combine the results coming from the proposed ontology-based retrieval model and the result returned by traditional keyword-based model. The combined model is evaluated using both traditional IR measures.
|
| title |
Fusion Model for Ontology Based Image Retrieval
|
| title_full |
Fusion Model for Ontology Based Image Retrieval
|
| title_fullStr |
Fusion Model for Ontology Based Image Retrieval
|
| title_full_unstemmed |
Fusion Model for Ontology Based Image Retrieval
|
| title_short |
Fusion Model for Ontology Based Image Retrieval
|
| title_sort |
fusion model for ontology based image retrieval
|