It has become customary that practically any information can be in a digital form. However, searching for relevant information can be complicated because of: (1) the diversity of ways in which specific data can be sorted, compared, related, or classified, and (2) the exponentially increasing amount of digital data. Accordingly, a successful search engine should address problems of extensibility and scalability. The Multi-Feature Indexing Network (MUFIN) is a general purpose search engine that satisfies these requirements. The extensibility is ensured by adopting the metric space to model the similarity, so MUFIN can evaluate queries over a wide variety of data domains compared by metric distance functions. The scalability is achieved by utilizing the paradigm of structured peer-to-peer networks, where the computational workload of query execution is distributed over multiple independent peers which can work in parallel. We demonstrate these unique capabilities of MUFIN on a database of 100 million images indexed according to a combination of five MPEG-7 descriptors.
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