CISE Research Instrumentation: Experimental Infrastructure for Indexing, Retrieval and Robust Presentation of Multimedia Data
Aidong Zhang Principal Investigator
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9818289<br/>Zhang, Aidong<br/>Srihari, Rohini K.<br/>State University of New York at Buffalo<br/><br/>CISE Research Infrastructure: Experimental Infrastructure for Indexing, Retrieval, and Robost Presentation of Multimedia Data<br/><br/>This research instrumentation enables research projects in:<br/><br/>- Consistent and Robust Retrieval, Transmission and Presentation of Multimedia Data<br/>- Multimedia Image annotation, Indexing and Retrieval, and <br/>- Multidimensional Data Mining and Applications in Large-Scale Multimedia Databases<br/><br/>This research instrumentation grant contributes to the purchase of a high performance storage system to establish an experimental infrastructure for conducting research on multimedia information management and retrieval. The requested instrumentation enables three projects which require high-capacity machines with large memory and huge disk space: supporting customized multimedia presentations in distributed environments; multimedia data annotation, indexing and retrieval; and data mining and applications in large-scale multimedia databases. The first project establishes an underlying framework and develops techniques for ensuring consistent and robust retrieval, transmission and presentation of multimedia data. Experimental research will be conducted on concurrent presentations of heterogeneous multimedia data, robust end-to-end transmission protocols, incorporation of user interactions and delay effects in synchronizing strategies. The second project investigates the integration of text and photographic information in both the indexing and retrieval stages of a large-scale picture retrieval system. Experimental analysis will be conducted to demonstrate the utility of these techniques. The third project investigates multi-resolution clustering approaches on vast amount of multi-dimensional data generated from real-life images. The performance of the clustering approaches will be studied and the effectiveness of these approaches will be demonstrated through different types of distributions of data.