Anna Saro Vijendran

Work place: Department of MCA, SNR Sons College, Coimbatore- 641 006, India

E-mail: saroviji@rediffmail.com

Website:

Research Interests: Artificial Intelligence, Neural Networks, Image Compression, Image Manipulation, Image Processing, Data Mining, Data Structures and Algorithms

Biography

Dr. Anna SaroVijendran received the Ph.D. degree in Computer Science from Mother Teresa Women’s University, Tamilnadu, India, in 2009. She has 24 years of experience in teaching. She is currently working as the Director, Dept of MCA in SNR Sons College, Coimbatore, Tamilnadu, India. She has presented and published many papers in International and National conferences. She has authored and co-authored more than 50 refereed papers. She has also acted as chair person in many National and International Conferences. Her professional interests are Image Processing, Image Fusion, Data Mining and Artificial Neural Networks.

Author Articles
SANB-SEB Clustering: A Hybrid Ontology Based Image and Webpage Retrieval for Knowledge Extraction

By Anna Saro Vijendran Deepa .C

DOI: https://doi.org/10.5815/ijitcs.2015.01.05, Pub. Date: 8 Dec. 2014

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.

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