Semantic Rule Based Image Visual Feature Ontology Creation

R. I. Minu; K. K. Thyagharajan

doi:10.1007/s11633-014-0832-3

October 2014

Article Contents

R. I. Minu and K. K. Thyagharajan. Semantic Rule Based Image Visual Feature Ontology Creation. International Journal of Automation and Computing, vol. 11, no. 5, pp. 489-499, 2014. doi: 10.1007/s11633-014-0832-3

Cite as: R. I. Minu and K. K. Thyagharajan. Semantic Rule Based Image Visual Feature Ontology Creation. International Journal of Automation and Computing, vol. 11, no. 5, pp. 489-499, 2014. doi: 10.1007/s11633-014-0832-3

Semantic Rule Based Image Visual Feature Ontology Creation

R. I. Minu^{1
,
,},
K. K. Thyagharajan²

Department of Computer Science & Engineering, Jerusalem College of Engineering, Tamil Nadu, India;
RMD Engineering College, Tamil Nadu, India

Received: 2014-01-15

Abstract

Multimedia is one of the important communication channels for mankind. Due to the advancement in technology and enormous growth of mankind, a vast array of multimedia data is available today. This has resulted in the obvious need for some techniques for retrieving these data. This paper will give an overview of ontology-based image retrieval system for asteroideae flower family domain. In order to reduce the semantic gap between the low-level visual features of an image and the high-level domain knowledge, we have incorporated a concept of multi-modal image ontology. So, the created asteroideae flower domain specific ontology would have the knowledge about the domain and the visual features. The visual features used to define the ontology are prevalent color, basic intrinsic pattern and contour gradient. In prevalent color extraction, the most dominant color from the images was identified and indexed. In order to determine the texture pattern for a particular flower, basic intrinsic patterns were used. The contour gradients provide the information on the image edges with respect to the image base. These feature values are embedded in the ontology at appropriate slots with respect to the domain knowledge. This paper also defines some of the query axioms which are used to retrieve appropriate information from the created ontology. This ontology can be used for image retrieval system in semantic web.
- Information and knowledge,
- computer vision,
- intelligent computing,
- feature extraction,
- ontology

References

[1]	G. Hiranmay, S. Chaudhury, A. Mallik. Ontology for multimediaapplications. IEEE Intelligent Informatics Bulletin,vol. 14, no. 1, pp. 21-30, 2013.
[2]	H. Ma, J. K. Zhu, M. R. T. Lyu, I. King. Bridging the semanticgap between image contents and tags. IEEE Transactionson Multimedia, vol. 12, no. 5, pp. 462-473, 2010.
[3]	R. Datta, D. Joshi, J. Li, J. Z.Wang. Image retrieval: Ideas,influences, and trends of the new age. ACM Computing surveys,vol. 4, no. 2, Article 5, 2008.
[4]	J. Sun, Y. J. Xing. An effective image retrieval mechanismusing family based spatial consistency filtration with objectregion. International Journal of Automation And Computing,vol. 7, no. 1, pp. 23-30, 2010.
[5]	G. Nagarajan, K. K. Thyagharajan. A novel image retrievalapproach for semantic web. International Journal of ComputerApplications, vol. 37, no. 8, pp. 10-14, 2012.
[6]	M. Flickner, H. Sawhney, W. Niblack, J. Ashley, Q. Huang,B. Dom, M. Gorkani. Query by image and video content:The QBIC system. Computer, vol. 28, no. 9, pp. 23-32,1995.
[7]	J. R. Smith, S. F. Chang. VisualSEEk: A fully automatedcontent-based image query system. In Proceedings of the4th ACM international conference on Multimedia, ACM,Boston, USA, pp. 87-98, 1997.
[8]	S. F. Chang, W. Chen, H. J. Meng, H. Sundaram, D. Zhong.VideoQ: An automated content based video search systemusing visual cues. In Proceedings of the 5th InternationalConference on Multimedia, ACM, Boxton, USA, pp. 313-324, 1997.
[9]	T. Huang, M. Sharad, R. Kannan. Multimedia analysis andretrieval system (MARS) project. In Proceedings on AnnualClinic on Library Applications of Data Processing-digitalImage Access and Retrieval, 1996.
[10]	C. Carson, S. Belongie, H. Greenspan, J. Malik. Blobworld:Image segmentation using expectation-maximization andits application to image querying. IEEE Transactions onPattern Analysis and Machine Intelligence, vol. 24, no. 8,pp. 1026-1038, 2002.
[11]	O. D. Robles, P. Toharia, A. Rodre guez, L. Pastor. Towardsa content-based video retrieval system using wavelet-basedsignatures. In Proceedings of the 7th International Conferenceon Computer Graphics and Imaging, IASTED, Kauai,Hawaii, USA, pp. 344-349. 2004.
[12]	C. G. M. Snoek, K. E. A. van de Sande, O. De Rooij, B.Huurnink, J. R. R. Uijlings, M. van Liempt, M. Bugalhoy, I.Trancoso, F. Yan, M. A. Tahir, K. Mikolajczyk, J. Kittler,M. de Rijke, J. M. Geusebroek, T. Gevers, M. Worring,A. W. M. Smeulders. The MediaMill TRECVID 2009 semanticvideo search engine. In Proceedings of TREC VideoRetvieval Evaluation Workshop, 2009.
[13]	Y. Liu, D. S. Zhang, G. J. Lu. Region based image retrievalwith high level semantic using decision tree learning. PatternRecognition, vol. 41, no. 8, pp. 2554-2570, 2008.
[14]	K. K. Thyagharajan, R. Harikrishnan. Content based bandwidthaware hierarchical video summarization. InternationalJournal of Computer Science, Systems Engineeringand Information Technology, vol. 2, no. 2, pp. 197-203,2009.
[15]	S. Romberg, R. Lienhart, E. Horster. Multimodal image retrievalfusing modalities with multilayer multlimodal pLSA.International Journal of Multimedia Information Retrieval,vol. 1, no. 1, pp. 31-44, 2012.
[16]	K. K. Thyagharajan, G. Nagarajan. Semantically effectivevisual concept illustration for images. International Journalof Future Computer and Communication, vol. 3, no. 2,pp. 124-128, 2013.
[17]	H. Wang, S. Liu, L. T. Chia. Does ontology help in imageretrieval?: A comparison between keyword, text ontologyand multi-modality ontology approaches. In Proceedings ofthe 14th Annual ACM International Conference on Multimedia,ACM, New York, pp. 109-112, 2006.
[18]	The Angiosperm Phylogeny Group. An update of the angiospermphylogeny group classification for the orders andfamilies of flowering plants: APG II. Botanical Journal ofthe Linnean Society, vol. 141, no. 4, pp. 399-436, 2003.
[19]	T. Saitoh, T. Kaneko. Automatic recognition of wild flowers.Systems and Computers in Japan, vol. 34, no. 10,pp. 90-101, 2003.
[20]	K. Fukuda, T. Takiguchi, A. Yasuo. Multiple classifierbased on fuzzy c-means for a flower image retrieval. In Proceedings of Workshop on Nonlinear Circuits and Signal Processing,Bangkok, Thailand, pp. 76-79. 2008.
[21]	M. E. Nilsback, A. Zisserman. Automated flower classificationover a large number of classes. In Proceedings of the 6th Indian Conference on Computer Vision, Graphics and ImageProcessing, IEEE, Bhubaneswar, Indian, pp. 722-729,2008.
[22]	Y. N. Chai. Recognition Between a Large Number of FlowerSpecies, Ph. D. dissertation, University of Oxford, UK,2011.
[23]	R. I. Minu, K. K. Thyagarajan. A novel approach tobuild image ontology using texton. In Proceedings of InternationalSymposium on Intelligent Informatics, Springer,Chennai, India, vol. 182, pp. 333-339. 2013.
[24]	K. K. Thyagharajan, R. I. Minu. Prevalent color extractionand indexing. International Journal of Engineering andTechnology, vol. 5, no. 6, pp. 4841-4849, 2013.
[25]	S. C. Zhu, C. E. Guo, Y. Z. Wang, Z. J. Xu. What are Textons?International Journal of Computer Vision, vol. 62,no. 1-2, pp. 121-143, 2005.
[26]	S. Russell, P. Norvig. Artificial Intelligence: A Modern Approach,2nd ed., Pearson Education Asia, 2011.
[27]	L. Liu, F. Yang, P. Zhang, J. Y. Wu, L. Hu. SVM basedontology matching approach. International Journal of Automationand Computing, vol. 9, no. 3, pp. 306-314, 2012.
[28]	T. Eiter, T. Lukasiewicz, R. Schindlauer, H. Tompits. Wellfoundedsemantics for description logic programs in the semanticweb. In Proceedings of International Workshop onRules and Rule Markup Languages for the Semantic Web,Lecture Notes in Computer Science, Springer, Hiroshima,Japan, vol. 3323, pp. 81-97, 2004.
[29]	K. Dellschaft, S. Staab. On how to perform a gold standardbased evaluation of ontology learning. In Proceedingsof Semantic Web Conference, Lecture Noates in ComputerScience, Springer, Athens, GA, USA, vol. 4273, pp. 228-241.2006.

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Semantic Rule Based Image Visual Feature Ontology Creation

Department of Computer Science & Engineering, Jerusalem College of Engineering, Tamil Nadu, India;

RMD Engineering College, Tamil Nadu, India

Received: 2014-01-15

Key words:

Abstract: Multimedia is one of the important communication channels for mankind. Due to the advancement in technology and enormous growth of mankind, a vast array of multimedia data is available today. This has resulted in the obvious need for some techniques for retrieving these data. This paper will give an overview of ontology-based image retrieval system for asteroideae flower family domain. In order to reduce the semantic gap between the low-level visual features of an image and the high-level domain knowledge, we have incorporated a concept of multi-modal image ontology. So, the created asteroideae flower domain specific ontology would have the knowledge about the domain and the visual features. The visual features used to define the ontology are prevalent color, basic intrinsic pattern and contour gradient. In prevalent color extraction, the most dominant color from the images was identified and indexed. In order to determine the texture pattern for a particular flower, basic intrinsic patterns were used. The contour gradients provide the information on the image edges with respect to the image base. These feature values are embedded in the ontology at appropriate slots with respect to the domain knowledge. This paper also defines some of the query axioms which are used to retrieve appropriate information from the created ontology. This ontology can be used for image retrieval system in semantic web.

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Reference (29)

[1]	G. Hiranmay, S. Chaudhury, A. Mallik. Ontology for multimediaapplications. IEEE Intelligent Informatics Bulletin,vol. 14, no. 1, pp. 21-30, 2013.
[2]	H. Ma, J. K. Zhu, M. R. T. Lyu, I. King. Bridging the semanticgap between image contents and tags. IEEE Transactionson Multimedia, vol. 12, no. 5, pp. 462-473, 2010.
[3]	R. Datta, D. Joshi, J. Li, J. Z.Wang. Image retrieval: Ideas,influences, and trends of the new age. ACM Computing surveys,vol. 4, no. 2, Article 5, 2008.
[4]	J. Sun, Y. J. Xing. An effective image retrieval mechanismusing family based spatial consistency filtration with objectregion. International Journal of Automation And Computing,vol. 7, no. 1, pp. 23-30, 2010.
[5]	G. Nagarajan, K. K. Thyagharajan. A novel image retrievalapproach for semantic web. International Journal of ComputerApplications, vol. 37, no. 8, pp. 10-14, 2012.
[6]	M. Flickner, H. Sawhney, W. Niblack, J. Ashley, Q. Huang,B. Dom, M. Gorkani. Query by image and video content:The QBIC system. Computer, vol. 28, no. 9, pp. 23-32,1995.
[7]	J. R. Smith, S. F. Chang. VisualSEEk: A fully automatedcontent-based image query system. In Proceedings of the4th ACM international conference on Multimedia, ACM,Boston, USA, pp. 87-98, 1997.
[8]	S. F. Chang, W. Chen, H. J. Meng, H. Sundaram, D. Zhong.VideoQ: An automated content based video search systemusing visual cues. In Proceedings of the 5th InternationalConference on Multimedia, ACM, Boxton, USA, pp. 313-324, 1997.
[9]	T. Huang, M. Sharad, R. Kannan. Multimedia analysis andretrieval system (MARS) project. In Proceedings on AnnualClinic on Library Applications of Data Processing-digitalImage Access and Retrieval, 1996.
[10]	C. Carson, S. Belongie, H. Greenspan, J. Malik. Blobworld:Image segmentation using expectation-maximization andits application to image querying. IEEE Transactions onPattern Analysis and Machine Intelligence, vol. 24, no. 8,pp. 1026-1038, 2002.
[11]	O. D. Robles, P. Toharia, A. Rodre guez, L. Pastor. Towardsa content-based video retrieval system using wavelet-basedsignatures. In Proceedings of the 7th International Conferenceon Computer Graphics and Imaging, IASTED, Kauai,Hawaii, USA, pp. 344-349. 2004.
[12]	C. G. M. Snoek, K. E. A. van de Sande, O. De Rooij, B.Huurnink, J. R. R. Uijlings, M. van Liempt, M. Bugalhoy, I.Trancoso, F. Yan, M. A. Tahir, K. Mikolajczyk, J. Kittler,M. de Rijke, J. M. Geusebroek, T. Gevers, M. Worring,A. W. M. Smeulders. The MediaMill TRECVID 2009 semanticvideo search engine. In Proceedings of TREC VideoRetvieval Evaluation Workshop, 2009.
[13]	Y. Liu, D. S. Zhang, G. J. Lu. Region based image retrievalwith high level semantic using decision tree learning. PatternRecognition, vol. 41, no. 8, pp. 2554-2570, 2008.
[14]	K. K. Thyagharajan, R. Harikrishnan. Content based bandwidthaware hierarchical video summarization. InternationalJournal of Computer Science, Systems Engineeringand Information Technology, vol. 2, no. 2, pp. 197-203,2009.
[15]	S. Romberg, R. Lienhart, E. Horster. Multimodal image retrievalfusing modalities with multilayer multlimodal pLSA.International Journal of Multimedia Information Retrieval,vol. 1, no. 1, pp. 31-44, 2012.
[16]	K. K. Thyagharajan, G. Nagarajan. Semantically effectivevisual concept illustration for images. International Journalof Future Computer and Communication, vol. 3, no. 2,pp. 124-128, 2013.
[17]	H. Wang, S. Liu, L. T. Chia. Does ontology help in imageretrieval?: A comparison between keyword, text ontologyand multi-modality ontology approaches. In Proceedings ofthe 14th Annual ACM International Conference on Multimedia,ACM, New York, pp. 109-112, 2006.
[18]	The Angiosperm Phylogeny Group. An update of the angiospermphylogeny group classification for the orders andfamilies of flowering plants: APG II. Botanical Journal ofthe Linnean Society, vol. 141, no. 4, pp. 399-436, 2003.
[19]	T. Saitoh, T. Kaneko. Automatic recognition of wild flowers.Systems and Computers in Japan, vol. 34, no. 10,pp. 90-101, 2003.
[20]	K. Fukuda, T. Takiguchi, A. Yasuo. Multiple classifierbased on fuzzy c-means for a flower image retrieval. In Proceedings of Workshop on Nonlinear Circuits and Signal Processing,Bangkok, Thailand, pp. 76-79. 2008.
[21]	M. E. Nilsback, A. Zisserman. Automated flower classificationover a large number of classes. In Proceedings of the 6th Indian Conference on Computer Vision, Graphics and ImageProcessing, IEEE, Bhubaneswar, Indian, pp. 722-729,2008.
[22]	Y. N. Chai. Recognition Between a Large Number of FlowerSpecies, Ph. D. dissertation, University of Oxford, UK,2011.
[23]	R. I. Minu, K. K. Thyagarajan. A novel approach tobuild image ontology using texton. In Proceedings of InternationalSymposium on Intelligent Informatics, Springer,Chennai, India, vol. 182, pp. 333-339. 2013.
[24]	K. K. Thyagharajan, R. I. Minu. Prevalent color extractionand indexing. International Journal of Engineering andTechnology, vol. 5, no. 6, pp. 4841-4849, 2013.
[25]	S. C. Zhu, C. E. Guo, Y. Z. Wang, Z. J. Xu. What are Textons?International Journal of Computer Vision, vol. 62,no. 1-2, pp. 121-143, 2005.
[26]	S. Russell, P. Norvig. Artificial Intelligence: A Modern Approach,2nd ed., Pearson Education Asia, 2011.
[27]	L. Liu, F. Yang, P. Zhang, J. Y. Wu, L. Hu. SVM basedontology matching approach. International Journal of Automationand Computing, vol. 9, no. 3, pp. 306-314, 2012.
[28]	T. Eiter, T. Lukasiewicz, R. Schindlauer, H. Tompits. Wellfoundedsemantics for description logic programs in the semanticweb. In Proceedings of International Workshop onRules and Rule Markup Languages for the Semantic Web,Lecture Notes in Computer Science, Springer, Hiroshima,Japan, vol. 3323, pp. 81-97, 2004.
[29]	K. Dellschaft, S. Staab. On how to perform a gold standardbased evaluation of ontology learning. In Proceedingsof Semantic Web Conference, Lecture Noates in ComputerScience, Springer, Athens, GA, USA, vol. 4273, pp. 228-241.2006.

Semantic Rule Based Image Visual Feature Ontology Creation

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