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This text presents proceedings from the first Australian Data Fusion Symposium in 1996. Topics include multi-sensor tracking, algorithms and architecture, radar, and military data fusion systems.
The two-volume set LNCS 4141, and LNCS 4142 constitutes the refereed proceedings of the Third International Conference on Image Analysis and Recognition, ICIAR 2006. The volumes present 71 revised full papers and 92 revised poster papers together with 2 invited lectures. Volume I includes papers on image restoration and enhancement, image segmentation, image and video processing and analysis, image and video coding and encryption, image retrieval and indexing, and more.
The technique of data fusion has been used extensively in information retrieval due to the complexity and diversity of tasks involved such as web and social networks, legal, enterprise, and many others. This book presents both a theoretical and empirical approach to data fusion. Several typical data fusion algorithms are discussed, analyzed and evaluated. A reader will find answers to the following questions, among others: What are the key factors that affect the performance of data fusion algorithms significantly? What conditions are favorable to data fusion algorithms? CombSum and CombMNZ, which one is better? and why? What is the rationale of using the linear combination method? How can the best fusion option be found under any given circumstances?
Web search has significantly evolved in recent years. For many years, web search engines such as Google and Yahoo! were only providing search service over text documents. Aggregated search was one of the first steps to go beyond text search, and was the beginning of a new era for information seeking and retrieval. These days, web search engines support aggregated search over a number of verticals, and blend different types of documents (e.g. images, videos) in their search results. Moreover, web search engines have started to crawl and search the hidden web. Federated search (federated information retrieval or distributed information retrieval) has played a key role in providing the technology for aggregated search and crawling the hidden web. The application of federated search is not limited to the web search engines. There are many scenarios such as digital libraries in which information is distributed across different sources/servers. Peer-to-peer networks and personalized search are two examples in which federated search has been successfully used for searching multiple independent collections. Federated Search provides a comprehensive summary of the research done to date, looks at some of the challenges still to be faced, and suggests some directions for future research on this important and current topic.