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

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

ISBN-10: 3540798595

ISBN-13: 9783540798590

This e-book 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 equipped 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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Read or Download Adaptive Multimedia Retrieval: Retrieval, User, and Semantics: 5th International Workshop, AMR 2007, Paris, France, July 5-6, 2007 Revised Selected Papers PDF

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

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Although it happened that for some queries predicted terms also included irrelevant ones, the precision of Imagination was better than that of MMG alone on each query image, thus validating the effectiveness of correlation analysis. In Figure 8 an example of Imagination in action is reported. In this case, the optimal terms that Imagination returns are sheep and grass, which are indeed the only appropriate ones among the P T = 6 predicted by MMG. Finally, Table 3 id: 448 Fig. 8. The maximum weight clique for the image on the right 42 I.

4 Analyzing Correlations of Terms The approach we take to overcome MMG limitations is to perform a link analysis on a sub-graph of GMMG so as to find highly-correlated terms. In turn, this is evidence that such terms are also semantically related, thus good candidates to annotate a new image. , GT is derived from GMMG by deleting region nodes. With the aim of estimating the similarity between couples of terms, we derive from GT a second-order (bipartite) graph G2T = (VT2 , ET2 ). 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 .

The semantic function is, in this case, the equilibrium of that cycle or, in other terms, the fix-point of the function μ(D) ◦ u. These examples show how a pair of current—and very different—approaches to signification can be placed in this general theoretical framework. The full use of it, however, entails the use of some prior context that can specify, through the specification of the desired final context, the sought meaning, or that can be used as the starting point of a emergent semantic cycle.

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