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Asian Journal of Information Management

Year: 2010 | Volume: 4 | Issue: 1 | Page No.: 12-18
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Authors

V. Prasannakumari

Keywords

Research Article

Contextual Information Retrieval for Multi-Media Databases with Learning by Feedback Using Vector Space Model

V. Prasannakumari
Information retrieval from huge databases has been an area of research to improve efficiency in terms of performance and precision of results returned. This study deals with contextual information retrieval from multimedia databases which have feature descriptors and metadata for the data items in it. This approach uses vector space method for IR and uses a stemming process to throttle feature descriptors to root words which in turn increases efficiency. Learning by feedback enables the database to accommodate more feature descriptors related to the data and builds it as a better described database. We have confined our study to IR alone excluding the considerations of data representation and storage techniques. Proposed method provides efficient search results and adds relevant contextual information to data for improvisation.
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