Adaptive Multimedia Retrieval:User, Context, and Feedback: - download pdf or read online

By Stéphane Marchand-Maillet, Eric Bruno

ISBN-10: 3540715444

ISBN-13: 9783540715443

ISBN-10: 3540715452

ISBN-13: 9783540715450

This publication constitutes the completely refereed post-proceedings of the 4th foreign Workshop on Adaptive Multimedia Retrieval, AMR 2006, held in Geneva, Switzerland in July 2006.

The 18 revised complete papers provided including 2 invited papers have been rigorously chosen in the course of rounds of reviewing and development. additionally integrated are invited contributions which have been meant to open on less-addressed subject matters in the neighborhood, because it is the case for song info retrieval and allotted details retrieval. The papers are prepared in topical sections on ontology-based retrieval and annotation, rating and similarity measurements, song info retrieval, visible modelling, adaptive retrieval, structuring multimedia, in addition to consumer integration and profiling.

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Read or Download Adaptive Multimedia Retrieval:User, Context, and Feedback: 4th International Workshop, AMR 2006, Geneva, Switzerland, July, 27-28, 2006, Revised ... Applications, incl. Internet/Web, and HCI) PDF

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Additional resources for Adaptive Multimedia Retrieval:User, Context, and Feedback: 4th International Workshop, AMR 2006, Geneva, Switzerland, July, 27-28, 2006, Revised ... Applications, incl. Internet/Web, and HCI)

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20. Wei Wei and King Ngi Ngan. Automatic video object segmentation for mpeg-4. In VCIP, pages 9–19, 2003. ch Ecole Polytechnique F´ed´erale de Lausanne (EPFL), Switzerland 1 2 Abstract. This work presents a discriminative model for the retrieval of pictures from text queries. The core idea of this approach is to minimize a loss directly related to the retrieval performance of the model. For that purpose, we rely on a ranking loss which has recently been successfully applied to text retrieval problems.

The extracted information will be stored as XMLs with their related frames. Also we can improve the domain ontology with the information gained from speech-text. Because any event can be expressed by more than one word during speech, all possible words can be expressed in the ontology to relate them with each other. For instance, the announcer can say kick the ball or hit the ball as the starter event of the free-kick event where we can figure out both means the same event from the ontology. Lets consider the query statement Retrieve all frames where a free-kick goal happens.

As the image as a whole can be seen as a region as well, a global image annotation can be attached likewise. Typically, an image can be split into atomic regions representing the smallest semantic entities. These entities can be grouped into superordinate regions depicting a more abstract view on an image. In general, any image can be split into groups of semantic regions which likewise can be split again until the atomic level is reached. Our annotation framework supports this idea by enabling the user to group newly created regions into sematic units of higher abstraction.

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Adaptive Multimedia Retrieval:User, Context, and Feedback: 4th International Workshop, AMR 2006, Geneva, Switzerland, July, 27-28, 2006, Revised ... Applications, incl. Internet/Web, and HCI) by Stéphane Marchand-Maillet, Eric Bruno


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