By Alan Hanjalic (auth.), Klaus Schoeffmann, Bernard Merialdo, Alexander G. Hauptmann, Chong-Wah Ngo, Yiannis Andreopoulos, Christian Breiteneder (eds.)
This ebook constitutes the refereed court cases of the 18th overseas Multimedia Modeling convention, MMM 2012, held in Klagenfurt, Austria, in January 2012. The 38 revised typical papers, 12 specified 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 functions, occasion and task, mining and cellular multimedia functions, seek, summarization and visualization, visualization and complex multimedia structures, and the targeted periods: interactive and immersive leisure and conversation, multimedia upkeep: the way to determine multimedia entry over the years, multi-modal and cross-modal seek, and video surveillance.
Read Online or Download Advances in Multimedia Modeling: 18th International Conference, MMM 2012, Klagenfurt, Austria, January 4-6, 2012. Proceedings PDF
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Extra info for Advances in Multimedia Modeling: 18th International Conference, MMM 2012, Klagenfurt, Austria, January 4-6, 2012. Proceedings
1 TagProp The label prediction Ll (Y ) for a test image Y is based on a weighted sum over the training images: N tagpropl (Y ) = p(Ll (Y )|I) = πyi p(Ll (Ii )) (1) i=1 Where p(Ll (Ii )) = 1 − for Ll (Ii ) = 1, otherwise. In  several variants for πyi are presented. We summarize here the best performing variant, which produces stateof-the-art results. Specifically, the weights πyi are πyi = exp (−dw (Y,Ii )) j exp(−dw (Y,Ij )) with dw (Y, i) = wT dyi (2) where dyi is a vector of base distances between Y and Ii .
The training set consists of 4500 images while the test set has 499 images, which we use to evaluate image-level prediction. There is no pixel-level annotation for this dataset. To describe images globally, we adopt the features of . They consist of GIST, color histograms (RGB, LAB, HSV) with 16 bins per channel, and bag-of-features histograms. For the latter, SIFT and Hue  descriptors are computed on a multiscale grid of points and at Harris interest points. These descriptors are quantized using K-means with 1000 centroids for SIFT and 100 for Hue.
To build such graphs two questions have to be addressed: i) the choice of feature points sets X and ii) the design of connectivity as edges E. To define the feature point sets X upon which graphs will be built we are looking for a set of feature points that we call the “seeds”. Around them, other feature points will be selected to build each graph feature. Selected seeds have to form a set of SURF points which are more likely to be detected in various instances of the same object. SURF points are detected where local maxima of the response of the approximated Hessian determinant are reached .