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

The 18 revised complete papers provided including 2 invited papers have been rigorously chosen in the course of rounds of reviewing and development. The papers are geared up in topical sections on snapshot annotation, suggestions and consumer modelling, tune retrieval, fusion, P2P and middleware, databases and summarization, in addition to ontology and semantics.

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This e-book constitutes the completely refereed post-workshop lawsuits 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 rigorously chosen in the course of rounds of reviewing and development.

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The properties of the “space of contexts” depend crucially on the properties of the representation of the context that we have chosen, and it is therefore difficult to say something more about meaning is we don’t impose some additional restriction. A reasonable one seems to be that we be capable of measuring the degree by which two contexts differ by means of an operation Δ(C1 , C2 ) ≥ 0 such that, for each context C, it is Δ(C, C) = 0. We don’t require, for the time being, that Δ be a distance.

A node in VT2 is either a pair of images (Ii , Ij ), Ii , Ij ∈ I, or a pair of terms (Tr , Ts ), Tr , Ts ∈ T . An edge between nodes (Ii , Ij ) and (Tr , Ts ) is added to ET2 iﬀ the two edges (Ii , Tr ) and (Ij , Ts ) (equivalently, (Ii , Ts ) and (Ij , Tr )) are both in ET . This is to say that each image Ii and Ij contains (at least) one of the two terms, and the two images, when taken together, contain both terms. Notice that when Ii = Ij , then terms Tr and Ts appear together in image Ii . An intuitive example of GT and of the derived G2T graph are depicted in Figures 5 (a) and (b), respectively.

In Section 5 we show how we derive the most correlated aﬃne terms and in Section 6 we provide some preliminary results obtained from Imagination. Section 7 concludes and discusses possible extensions. 2 Problem Deﬁnition Before presenting our image annotation technique, we need to precisely deﬁne the problem. We are given a dataset of N manually annotated images that constitute the image training set I. Each image Ii ∈ I is characterized as a set of regions Rj , for each of which a D-dimensional feature vector is automatically extracted.