Jan Sedmidubský

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As the number of digital images is growing fast and Content-based Image Retrieval (CBIR) is gaining in popularity, CBIR systems should leap towards Web-scale datasets. In this paper, we report on our experience in building an experimental similarity search system on a test collection of more than 50 million images. The first big challenge we have been(More)
We analyze routing mechanisms of a self-organizing semantic overlay for content-based search in multimedia data. This overlay operates over any existing P2P network based on the metric space approach. In particular, we replace the previous design of routing mechanisms in Metric Semantic Overlay (MSO) with a new adaptive query-routing algorithm. An advantage(More)
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(More)
Exploiting the concepts of social networking represents a novel approach to the approximate similarity query processing. We present an unstructured and dynamic P2P environment in which a metric social network is built. Social communities of peers giving similar results to specific queries are established and such ties are exploited for answering future(More)
Due to the exponential growth of digital data and its complexity, we need a technique which allows us to search such collections efficiently. A suitable solution seems to be based on the peer-to-peer (P2P) network paradigm and the metric-space model of similarity. During the building phase of the distributed structure, the peers often split as new peers(More)
In this paper we tackle the issues of exploiting the concepts of social networking in processing similarity queries in the environment of a P2P network. The processed similarity queries are laying the base on which the relationships among peers are created. Consequently, the communities encompassing similar data emerge in the network. The architecture of(More)
We propose a self-organized content-based Image Retrieval Network (IRN) that is inspired by a Metric Social Network (MSN) search system. The proposed network model is strictly data-owner oriented so no data redistribution among peers is needed in order to efficiently process queries. Thus a shared database where each peer is fully in charge of its data, is(More)