期刊文献+

Semantic Rule Based Image Visual Feature Ontology Creation 被引量:2

Semantic Rule Based Image Visual Feature Ontology Creation
原文传递
导出
摘要 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. 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.
出处 《International Journal of Automation and computing》 EI CSCD 2014年第5期489-499,共11页 国际自动化与计算杂志(英文版)
关键词 Information and knowledge computer vision intelligent computing feature extraction ONTOLOGY Information and knowledge computer vision intelligent computing feature extraction ontology
  • 相关文献

参考文献2

二级参考文献31

  • 1C.Carson,M.Thomas,S.Belongie,J.M.Hellerstein,J.Malik.Blobworld:A system for region-based image indexing and retrieval.In Proceeding of the 3rd International Conference on Visual Information Systems,IEEE Computer Society,Amsterdam,Holand,vol.2,pp.509-516,1999.
  • 2J.Sivic,A.Zisserman.Video google:A text retrieval approach to object matching in videos.In Proceedings of the 9th IEEE International Conference on Computer Vision,IEEE,Nice,France,vol.2,pp.1470-1477,2003.
  • 3J.Philbin,O.Chum,M.Isard,J.Sivic,A.Zisserman.Object retrieval with large vocabularies and fast spatial matching.In Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition,IEEE,Minneapolis,USA,pp.1-8,2007.
  • 4K.Gao,S.X.Lin,J.B.Guo,D.M.Zhang,Y.D.Zhang,Y.F.Wu.Object retrieval based on spatially frequent items with informative patches.In Proceedings of IEEE International Conference on Multimedia and Expo,IEEE,Hanoverian,Germany,pp.1305-1308,2008.
  • 5S.Lazebnik,C.Schmid,J.Ponce.Beyond bags of features:Spatial pyramid matching for recognizing natural scene categories.In Proceedings of IEEE Computer Society Conference on Conference on Computer Vision and Pattern Recognition,IEEE,New York,USA,vol.2,pp.2169-2178,2006.
  • 6Q.F.Zheng,W.Q.Wang,W.Gao.Effective and efficient object-based image retrieval using visual phrases.In Proceedings of the 14th Annual ACM International Conference on Multimedia,ACM,Santa Barbara,USA,pp.77-80,2006.
  • 7D.Nister,H.Stewenius.Scalable recognition with a vocabulary tree.In Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition,IEEE,New York,USA,vol.2,pp.2161-2168,2006.
  • 8C.Schmid,R.Mohr.Local grayvalue invariants for image retrieval.IEEE Transactions on Pattern Analysis and Mahine Intelligence,vol.19,no.5,pp.530-535,1997.
  • 9T.Tuytelaars,L.Van Gool.Content-based image retrieval based on local affinely invariant regions.Lecture Notes in Computer Science,Springer,vol.1614,pp.493-500,1999.
  • 10F.Schaffalitzky.A.Zisserman.Multi-view matching for unordered image sets.In Proceedings of the 7th European Conference on Computer Vision,Lecture Notes in Computer Science,Springer,Copenhagen,Denmark,vol.2350,pp.414-431,2002.

共引文献2

同被引文献12

引证文献2

二级引证文献2

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部