Read e-book online Adaptive Multimedia Retrieval: Retrieval, User, and PDF

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

ISBN-10: 3540798595

ISBN-13: 9783540798590

This ebook 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 awarded including 2 invited papers have been conscientiously chosen in the course of rounds of reviewing and development. The papers are geared up in topical sections on photograph annotation, suggestions and person modelling, track 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 info for Adaptive Multimedia Retrieval: Retrieval, User, and Semantics: 5th International Workshop, AMR 2007, Paris, France, July 5-6, 2007 Revised Selected Papers

Sample text

For instance, features could represent the color and the texture of Rj [2]. Moreover, each image Ii ∈ I is manually annotated with mi terms {Ti1 , . . , Timi }. Thus, each image Ii is represented as Ii = ({Ri1 , . . , Rini }, {Ti1 , . . , Timi }). Problem 1. Given an unlabelled (or query) image Iq , with regions {Rq1 , . . , Rqnq }, exploit the knowledge of images in I to predict a “good” set of terms {Tq1 , . . , Tqmq } able to effectively characterize the content of Iq . We turn the annotation problem, an instance of which is depicted in Figure 2, into a graph-based problem that is split into three main steps: 1.

Visual example of the image annotation problem 2. Correlation of terms: Starting from GMMG , we derive a second-order graph G2T from which to compute the similarity among terms. 3. Correlated affine terms: In this step we combine the results of the first two steps and derive the set of most semantically correlated terms to label the query image Iq . 3 Affinities of Terms and Query Image As for the implementation of the 1st step, we follow the Mixed Media Graph approach [13]. 1 Graph Construction The Mixed Media Graph (MMG) GMMG = (V, E) is a 3-level undirected graph, where each node represents an image (identifier), a region, or a term, in the training set.

The context in which the data assume a meaning is always given by a human activity, not only because of the collateral data that are the support and the product of the activity but also (an aspect that I haven’t considered in this paper) because of the discursive practices of that activity, an aspect that Jain and Scherp called expressive semantics (Scherp & Jain, 2007). The context, and especially the discursive practices in it, is so important that one can’t quite pin down a single generic activity called “search” that can be applied to all human activities: looking for a text to elucidate a point to ourselves while writing a scientific paper is not the same activity as looking for something to read before going to bed.

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Adaptive Multimedia Retrieval: Retrieval, User, and Semantics: 5th International Workshop, AMR 2007, Paris, France, July 5-6, 2007 Revised Selected Papers by Josip Krapac, Frédéric Jurie (auth.), Nozha Boujemaa, Marcin Detyniecki, Andreas Nürnberger (eds.)


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