SANB-SEB Clustering: A Hybrid Ontology Based Image and Webpage Retrieval for Knowledge Extraction
Автор: Anna Saro Vijendran, Deepa .C
Журнал: International Journal of Information Technology and Computer Science(IJITCS) @ijitcs
Статья в выпуске: 1 Vol. 7, 2015 года.
Бесплатный доступ
Data mining is a hype-word and its major goal is to extract the information from the dataset and convert it into readable format. Web mining is one of the applications of data mining which helps to extract the web page. Personalized image was retrieved in existing systems by using tag-annotation-demand ranking for image retrieval (TAD) where image uploading, query searching, and page refreshing steps were taken place. In the proposed work, both the image and web page are retrieved by several techniques. Two major steps are followed in this work, where the primary step is server database upload. Herein, database for both image and content are stored using block acquiring page segmentation (BAPS). The subsequent step is to extract the image and content from the respective server database. The subsequent database is further applied into semantic annotation based clustering (SANB) (for image) and semantic based clustering (SEB) (for content). The experimental results show that the proposed approach accurately retrieves both the images and relevant pages.
Web Structure Mining, Ontology, Semantic Annotation, Block Acquiring Page Segmentation (BAPS), Semantic Annotation Based Clustering (SANB), Semantic Based Clustering (SEB)
Короткий адрес: https://sciup.org/15012220
IDR: 15012220
Список литературы SANB-SEB Clustering: A Hybrid Ontology Based Image and Webpage Retrieval for Knowledge Extraction
- M. Lux, et al., "Using visual features to improve tag suggestions in image sharing sites," Proceedings of knowledge acquisition from the social web, Graz, Austria, 2008.
- A. Sieg, et al., "Learning Ontology-Based User Profiles: A Semantic Approach to Personalized Web Search," IEEE Intelligent Informatics Bulletin, vol. 8, no. 1, pp. 7-18, 2007.
- K. Lerman, et al., "Personalizing image search results on flickr," Intelligent Information Personalization, 2007.
- M. La Cascia, et al., "Combining textual and visual cues for content-based image retrieval on the world wide web," in Content-Based Access of Image and Video Libraries, 1998. Proceedings. IEEE Workshop on, 1998, pp. 24-28.
- J. Zhang, et al., "A personalized image retrieval based on visual perception," Journal of Electronics (China), vol. 25, no. 1, pp. 129-133, 2008.
- Y. Liu, et al., "A survey of content-based image retrieval with high-level semantics," Pattern Recognition, vol. 40, no. 1, pp. 262-282, 2007.
- M. Kodmelwar and P. Futane, "An Optimization Technique for Image Search in Social Sharing Websites."
- B. Bradshaw, "Semantic based image retrieval: a probabilistic approach," in Proceedings of the eighth ACM international conference on Multimedia, 2000, pp. 167-176.
- H. Müller, et al., "A review of content-based image retrieval systems in medical applications—clinical benefits and future directions," International journal of medical informatics, vol. 73, no. 1, pp. 1-23, 2004.
- J. Vogel and B. Schiele, "Semantic modeling of natural scenes for content-based image retrieval," International Journal of Computer Vision, vol. 72, no. 2, pp. 133-157, 2007.
- M. Klíma, et al., "DEIMOS–an open source image database," Radioengineering, vol. 20, no. 4, pp. 1016-1023, 2011.
- W. Hsu, et al., "An integrated color-spatial approach to content-based image retrieval," in Proceedings of the third ACM international conference on Multimedia, 1995, pp. 305-313.
- L. Dai, et al., "Large scale image retrieval with visual groups," in Proc. IEEE ICIP, 2013.
- J.-H. Su, et al., "Multi-modal image retrieval by integrating web image annotation, concept matching and fuzzy ranking techniques," International Journal of Fuzzy Systems, vol. 12, no. 2, pp. 136-149, 2010.
- E. Hyvönen, et al., "Ontology-Based Image Retrieval," in WWW (Posters), 2003.
- N. Singhal, et al., "Reducing Network Traffic and Managing Volatile Web Contents Using Migrating Crawlers with Table of Variable Information," World Applied Sciences Journal, vol. 19, no. 5, pp. 666-673, 2012.
- Y. Rui, et al., "Relevance feedback: a power tool for interactive content-based image retrieval," Circuits and Systems for Video Technology, IEEE Transactions on, vol. 8, no. 5, pp. 644-655, 1998.
- J. Tang, et al., "Image annotation by graph-based inference with integrated multiple/single instance representations," Multimedia, IEEE Transactions on, vol. 12, no. 2, pp. 131-141, 2010.
- H. Muller, et al., "Strategies for positive and negative relevance feedback in image retrieval," in Pattern Recognition, 2000. Proceedings. 15th International Conference on, 2000, pp. 1043-1046.
- C. Chen, et al., "Web media semantic concept retrieval via tag removal and model fusion," ACM Transactions on Intelligent Systems and Technology (TIST), vol. 4, no. 4, p. 61, 2013.
