Covers essential techniques in Text Based Information Retrieval (TBIR) and Content Based Image Retrieval (CBIR)
Describes a deep web crawler specification with indexer to improve coverage rate and retrieval performance
Shows that the n-gram thesaurus is a central component of any retrieval system
Includes a mathematical approach to query refinement, along with prediction for retrieval applications
Is supported by experimental results and analysis useful for the research community
Covers essential techniques in Text Based Information Retrieval (TBIR) and Content Based Image Retrieval (CBIR)
Describes a deep web crawler specification with indexer to improve coverage rate and retrieval performance
Shows that the n-gram thesaurus is a central component of any retrieval system
Includes a mathematical approach to query refinement, along with prediction for retrieval applications
Is supported by experimental results and analysis useful for the research community
Dr. S.G. Shaila is an Associate Professor at the Department of Computer Science & Engineering, and earned her Ph.D. in Computer Science from the National Institute of Technology, Tiruchirapalli, Tamil Nadu for a thesis on Multimedia Information Retrieval in Distributed Systems. She brings with her years of teaching and research experience. Her main areas of interest are Information Retrieval, Image Processing, Cognitive Science and Pattern Recognition.
Dr. A. Vadivel received his Master´s in Science from the National Institute of Technology Trichy (NITT) before completing a Master´s in Technology (MTech) and PhD at the Indian Institute of Technology (IIT), Kharagpur, India. He has 12 years of technical experience as a Network & Instrumentation Engineer at the IIT-Kharagpur, and twelve years of teaching experience at Bharathidhasan University and NITT. Currently, he is working as an Associate Professor at SRM University Amaravathi, AP. He has published papers in more than 90 international journals and conference proceedings. His research areas are Content-Based Image and Video Retrieval, Multimedia Information Retrieval from Distributed Environments, Medical Image Analysis, Object Tracking in Motion Video, and Cognitive Science. He received the Young Scientist Award from the Department of Science and Technology, Government of India in 2007, the Indo-US Research Fellow Award from the Indo-US Science and Technology Forum in 2008, and the Obama-Singh Knowledge Initiative Award in 2013.
This book offers comprehensive coverage of information retrieval by considering both Text Based Information Retrieval (TBIR) and Content Based Image Retrieval (CBIR), together with new research topics. The approach to TBIR is based on creating a thesaurus, as well as event classification and detection. N-gram thesaurus generation for query refinement offers a new method for improving the precision of retrieval, while event classification and detection approaches aid in the classification and organization of information using web documents for domain-specific retrieval applications. In turn, with regard to content based image retrieval (CBIR) the book presents a histogram construction method, which is based on human visual perceptions of color. The book´s overarching goal is to introduce readers to new ideas in an easy-to-follow manner.
Chapter 1. Architecture Specification of Rule-Based Deep Web Crawler with Indexer.- Chapter 2. Information Classification and Organization using Neuro-Fuzzy Model Event Retrieval. Chapter 3. N-Gram Thesaurus Generation for Query Expansion and Refinement using Tag Term Weight for Information Retrieval.- Chapter 4. Smooth Weighted Color Histogram using Human Visual Perception for CBIR Applications.- Chapter 5. Indexing and Encoding Color Histogram with Bin Overlapped Similarity Measure for Image Retrieval.- Chapter 6. Summary and Conclusion.
This book offers comprehensive coverage of information retrieval by considering both Text Based Information Retrieval (TBIR) and Content Based Image Retrieval (CBIR), together with new research topics. The approach to TBIR is based on creating a thesaurus, as well as event classification and detection. N-gram thesaurus generation for query refinement offers a new method for improving the precision of retrieval, while event classification and detection approaches aid in the classification and organization of information using web documents for domain-specific retrieval applications. In turn, with regard to content based image retrieval (CBIR) the book presents a histogram construction method, which is based on human visual perceptions of color. The book´s overarching goal is to introduce readers to new ideas in an easy-to-follow manner.
This book offers comprehensive coverage of information retrieval by considering both Text Based Information Retrieval (TBIR) and Content Based Image Retrieval (CBIR), together with new research topics. The approach to TBIR is based on creating a thesaurus, as well as event classification and detection. N-gram thesaurus generation for query refinement offers a new method for improving the precision of retrieval, while event classification and detection approaches aid in the classification and organization of information using web documents for domain-specific retrieval applications. In turn, with regard to content based image retrieval (CBIR) the book presents a histogram construction method, which is based on human visual perceptions of color. The book's overarching goal is to introduce readers to new ideas in an easy-to-follow manner.
Chapter 1. Architecture Specification of Rule-Based Deep Web Crawler with Indexer.- Chapter 2. Information Classification and Organization using Neuro-Fuzzy Model Event Retrieval. Chapter 3. N-Gram Thesaurus Generation for Query Expansion and Refinement using Tag Term Weight for Information Retrieval.- Chapter 4. Smooth Weighted Color Histogram using Human Visual Perception for CBIR Applications.- Chapter 5. Indexing and Encoding Color Histogram with Bin Overlapped Similarity Measure for Image Retrieval.- Chapter 6. Summary and Conclusion.
Dr. S.G. Shaila is an Associate Professor at the Department of Computer Science & Engineering, and earned her Ph.D. in Computer Science from the National Institute of Technology, Tiruchirapalli, Tamil Nadu for a thesis on Multimedia Information Retrieval in Distributed Systems. She brings with her years of teaching and research experience. Her main areas of interest are Information Retrieval, Image Processing, Cognitive Science and Pattern Recognition.
Dr. A. Vadivel received his Master's in Science from the National Institute of Technology Trichy (NITT) before completing a Master's in Technology (MTech) and PhD at the Indian Institute of Technology (IIT), Kharagpur, India. He has 12 years of technical experience as a Network & Instrumentation Engineer at the IIT-Kharagpur, and twelve years of teaching experience at Bharathidhasan University and NITT. Currently, he is working as an Associate Professor at SRM University Amaravathi, AP. He has published papersin more than 90 international journals and conference proceedings. His research areas are Content-Based Image and Video Retrieval, Multimedia Information Retrieval from Distributed Environments, Medical Image Analysis, Object Tracking in Motion Video, and Cognitive Science. He received the Young Scientist Award from the Department of Science and Technology, Government of India in 2007, the Indo-US Research Fellow Award from the Indo-US Science and Technology Forum in 2008, and the Obama-Singh Knowledge Initiative Award in 2013.
Über den Autor
Dr. S.G. Shaila is an Associate Professor at the Department of Computer Science & Engineering, and earned her Ph.D. in Computer Science from the National Institute of Technology, Tiruchirapalli, Tamil Nadu for a thesis on Multimedia Information Retrieval in Distributed Systems. She brings with her years of teaching and research experience. Her main areas of interest are Information Retrieval, Image Processing, Cognitive Science and Pattern Recognition.
Dr. A. Vadivel received his Master's in Science from the National Institute of Technology Trichy (NITT) before completing a Master's in Technology (MTech) and PhD at the Indian Institute of Technology (IIT), Kharagpur, India. He has 12 years of technical experience as a Network & Instrumentation Engineer at the IIT-Kharagpur, and twelve years of teaching experience at Bharathidhasan University and NITT. Currently, he is working as an Associate Professor at SRM University Amaravathi, AP. He has published papersin more than 90 international journals and conference proceedings. His research areas are Content-Based Image and Video Retrieval, Multimedia Information Retrieval from Distributed Environments, Medical Image Analysis, Object Tracking in Motion Video, and Cognitive Science. He received the Young Scientist Award from the Department of Science and Technology, Government of India in 2007, the Indo-US Research Fellow Award from the Indo-US Science and Technology Forum in 2008, and the Obama-Singh Knowledge Initiative Award in 2013.
Inhaltsverzeichnis
Chapter 1. Architecture Specification of Rule-Based Deep Web Crawler with Indexer.- Chapter 2. Information Classification and Organization using Neuro-Fuzzy Model Event Retrieval. Chapter 3. N-Gram Thesaurus Generation for Query Expansion and Refinement using Tag Term Weight for Information Retrieval.- Chapter 4. Smooth Weighted Color Histogram using Human Visual Perception for CBIR Applications.- Chapter 5. Indexing and Encoding Color Histogram with Bin Overlapped Similarity Measure for Image Retrieval.- Chapter 6. Summary and Conclusion.
Klappentext
This book offers comprehensive coverage of information retrieval by considering both Text Based Information Retrieval (TBIR) and Content Based Image Retrieval (CBIR), together with new research topics. The approach to TBIR is based on creating a thesaurus, as well as event classification and detection. N-gram thesaurus generation for query refinement offers a new method for improving the precision of retrieval, while event classification and detection approaches aid in the classification and organization of information using web documents for domain-specific retrieval applications. In turn, with regard to content based image retrieval (CBIR) the book presents a histogram construction method, which is based on human visual perceptions of color. The book¿s overarching goal is to introduce readers to new ideas in an easy-to-follow manner.
Covers essential techniques in Text Based Information Retrieval (TBIR) and Content Based Image Retrieval (CBIR)
Describes a deep web crawler specification with indexer to improve coverage rate and retrieval performance
Shows that the n-gram thesaurus is a central component of any retrieval system
Includes a mathematical approach to query refinement, along with prediction for retrieval applications
Is supported by experimental results and analysis useful for the research community
