Managing and searching distributed multidimensional annotations with large scale image data

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Abstract

Advanced imaging applications are producing large-scale image data for medical diagnosis, part visualization and inspection, content retrieval, analysis, e-reporting, and so on. In many cases, service and non-textual annotations are made on top of the raw images to provide essential information on regions of interest, diseases, defects, evaluations, comments, and etc. These applications pose several challenges: i) Large data volume and the latency of data transfer over the Internet leads to a performance bottleneck; ii) The applications need advanced query support such as similarity queries on the multidimensional annotation data; and iii) Local applications need synchronizing data with a remote central database. In our work, we develop a general distributed multimedia data management system that achieves these goals by providing: i) an intelligent multimedia content caching system to support smooth local applications; ii) a loosely coupled extensible multi-indexing server to support different types of multimedia queries including similarity queries; iii) unified multimedia data access interfaces for universal data access; and iv) an integrated architecture that brings these technologies together into a robust system. The system is now successfully used to support image-based inspection and diagnosis applications such as global part inspection and knowledge database guided medical diagnosis. © Springer-Verlag Berlin Heidelberg 2007.

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APA

Xia, T., Wang, F., Liu, P., & Palanivelu, S. (2007). Managing and searching distributed multidimensional annotations with large scale image data. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4577 LNCS, pp. 361–370). Springer Verlag. https://doi.org/10.1007/978-3-540-73417-8_44

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