This paper describes how parallel retrieval is implemented in the content-based visual information retrieval framework VizIR. Generally, two major use cases for parallelisation exist in visual retrieval systems: distributed querying and simultaneous multi-user querying. Distributed querying includes parallel query execution and querying multiple databases. Content-based querying is a two-step process: transformation of feature space to distance space using distance measures and selection of result set elements from distance space. Parallel distance measurement is implemented by sharing example media and query parameters between querying threads. In VizIR, parallelisation is heavily based on caching strategies. Querying multiple distributed databases is already supported by standard relational database management systems. The most relevant issues here are error handling and minimisation of network bandwidth consumption. Moreover, we describe strategies for distributed similarity measurement and content-based indexing. Simultaneous multi-user querying raises problems such as caching of querying results and usage of relevance feedback and user preferences for query refinement. We propose a 'real' multi-user querying environment that allows users to interact in defining queries and browse through result sets simultaneously. The proposed approach opens an entirely new field of applications for visual information retrieval systems.
H. Eidenberger: "Parallel Visual Information Retrieval in VizIR"; Talk: SPIE Information Technology and Communication Symposium, Philadelphia, USA; 10-21-2004 - 10-24-2004; in: "SPIE Information Technology and Communication Symposium", (2004).
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