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Adaptive Multimedia Retrieval: Retrieval, User, and by Josip Krapac, Frédéric Jurie (auth.), Nozha Boujemaa, Marcin

By Josip Krapac, Frédéric Jurie (auth.), Nozha Boujemaa, Marcin Detyniecki, Andreas Nürnberger (eds.)

This e-book constitutes the completely refereed post-workshop complaints of the fifth overseas Workshop on Adaptive Multimedia Retrieval, AMR 2007, held in Paris, France, in July 2007.

The 18 revised complete papers offered including 2 invited papers have been conscientiously chosen in the course of rounds of reviewing and development. The papers are prepared in topical sections on picture annotation, suggestions and person modelling, song retrieval, fusion, P2P and middleware, databases and summarization, in addition to ontology and semantics.

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Extra resources for Adaptive Multimedia Retrieval: Retrieval, User, and Semantics: 5th International Workshop, AMR 2007, Paris, France, July 5-6, 2007 Revised Selected Papers

Example text

Of course, this doesn’t mean that all transformations are admissible; to make a trivial example, our previous consideration on the constitutive nature of relations tell us that, since a symbol is identified only by differentiation with other symbols, one can’t have a signification system composed of a single symbol: a morphism that maps all the terms of a document or an ontology to a single one would not only destroy the meaning of that document, it would destroy the very idea of meaning even though, mathematically, we are in the presence of a homomorphism.

Let us get rid immediately of the idea that “dog” means DOG because of the three letters of which it is composed. There is absolutely nothing in the sequence /d/, /o/, and /g/ that is in any way connected to dogness. The fact that I can read it and understand that we are talking about a dog tells something about me and the linguistic community in which I function, but absolutely nothing about dogs or dogness. If you don’s speak Italian, to you the sequence /c/, /a/, /n/, and /e/ doesn’t mean anything, but to me it means the same thing (with some important distinguos that I will consider shortly).

Dashed edges define the clique with the maximum weight G is the sum of weights of the nodes in V , W (G ) = j∈V wj . The Maximum Weight Clique Problem (MWCP) is to find the clique, Gmax , with the maximum weight. The correspondence with our problem is almost immediate. , wj = uIq (Tj )). , (Tr , Ts ) ∈ E iff corr(Tr , Ts ) > c. 7 To give an example, Figure 6 shows a sample graph G in which P T = 6. Numbers within each node represent unnormalized steady state probabilities (normalizing would not change the net effect).

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