Download Advances in Multimedia Modeling: 18th International by Alan Hanjalic (auth.), Klaus Schoeffmann, Bernard Merialdo, PDF

By Alan Hanjalic (auth.), Klaus Schoeffmann, Bernard Merialdo, Alexander G. Hauptmann, Chong-Wah Ngo, Yiannis Andreopoulos, Christian Breiteneder (eds.)

ISBN-10: 3642273548

ISBN-13: 9783642273544

This ebook constitutes the refereed court cases of the 18th foreign Multimedia Modeling convention, MMM 2012, held in Klagenfurt, Austria, in January 2012. The 38 revised usual papers, 12 particular consultation papers, 15 poster consultation papers, and six demo consultation papers have been rigorously reviewed and chosen from 142 submissions. The papers are geared up within the following topical sections: annotation, annotation and interactive multimedia purposes, occasion and task, mining and cellular multimedia functions, seek, summarization and visualization, visualization and complex multimedia platforms, and the particular classes: interactive and immersive leisure and verbal exchange, multimedia renovation: how you can make certain multimedia entry through the years, multi-modal and cross-modal seek, and video surveillance.

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Additional resources for Advances in Multimedia Modeling: 18th International Conference, MMM 2012, Klagenfurt, Austria, January 4-6, 2012. Proceedings

Example text

1. e. many concepts are represented by few hundred of images among the 11321 images in the database (much more negative data than the positive ones for many concepts). To overcome this problem we used cross-validation, taking at each fold as many positive as negative images. 3. M is the number of new concepts created during the building of the hierarchy. For training the classifier of each concept in the hierarchy, we took all images of son nodes (of a given Fig. 3. The semantic hierarchy built on Pascal VOC’2010 dataset.

Most importantly, their training scenarios are radically different from ours, where ground-truth segment labels are available at training time. Therefore, they address a different task, known as semantic segmentation in the literature [14,20], which can be seen as the fully supervised version of segment-level annotation. Note how several earlier methods proposed for image label prediction actually perform segment-level annotation. Early methods based on probabilistic models [2,5,19] describe the image as an orderless bag of segments.

A) Concept Person           (b) Concept Tv_monitor Fig. 5. Precision/recall curves for hierarchical and flat classification on concepts "Person" and "TV_Monitor" concept) as positive and all images of son nodes of its immediate ancestor as negative. For example, to train a classifier for "Carnivore" all images of "Dog" and "Cat" are taken as positive while images of "Bird", "Sheep", "Horse" and "Cow" as negative.

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