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

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

This publication constitutes the refereed lawsuits of the 18th overseas Multimedia Modeling convention, MMM 2012, held in Klagenfurt, Austria, in January 2012. The 38 revised general papers, 12 specified consultation papers, 15 poster consultation papers, and six demo consultation papers have been conscientiously reviewed and chosen from 142 submissions. The papers are equipped within the following topical sections: annotation, annotation and interactive multimedia functions, occasion and task, mining and cellular multimedia purposes, seek, summarization and visualization, visualization and complicated multimedia platforms, and the designated classes: interactive and immersive leisure and communique, multimedia maintenance: the way to be 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

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We define fo our layers, the bottom one containing only the seed, and the top one containing a grap ph built upon the seed and its 9 nearest neighbors, see examples in Figure 2. 3 Graph Compariison In order to integrate these new n graph features in a Bag-of-Visual-Words frameworrk a dissimilarity measure and a clustering method have to be defined. In this section, we define the dissimilarity meaasure. We are dealing with attributed graphs, where noodes can be compared with respeect to their visual appearance.

Therefore, estimating (8) is easier than estimating the distribution p(Ll (s)|{Si }, I) directly. In the spirit of [7], we adopt a simple clustering approach, which assigns exactly one label zij to each segment sij of image Ii 1 if l = zij Ll (sij ) = (9) 0 otherwise. From a given segment-label assignment z we derive the empirical label-token distribution T (sij )=t p(Ll (t)|t, z) = Z Ll (sij ), ij where Z is the normalization factor and t is a token. (10) Combining Image-Level and Segment-Level Models for Automatic Annotation 21 EHDUVQRZ  VQRZ EHDU  
 VQRZ WUHHZDWHUVN\ WUHH WUHH WUHH VN\ WUHH ZDWHU     (a)      (b) Fig.

When each segment label is predicted as a multinomial or multiple Bernoulli distributions, it is natural to combine them, for instance using a mixture model. We detail two alternatives below. Let Y denote a test image and {yr } the set of its segments. 1 Maximum Prediction In this approach, we combine segment-level predictions into an image-level one by keeping, for each label, the largest prediction over the segments. This procedure takes advantage of the compositionality of segments. If two regions are predicted to have different labels, it indeed transfers both labels to the image.

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