By Alan Hanjalic (auth.), Klaus Schoeffmann, Bernard Merialdo, Alexander G. Hauptmann, Chong-Wah Ngo, Yiannis Andreopoulos, Christian Breiteneder (eds.)
This ebook constitutes the refereed lawsuits of the 18th foreign Multimedia Modeling convention, MMM 2012, held in Klagenfurt, Austria, in January 2012. The 38 revised normal papers, 12 designated consultation papers, 15 poster consultation papers, and six demo consultation papers have been conscientiously reviewed and chosen from 142 submissions. The papers are prepared within the following topical sections: annotation, annotation and interactive multimedia functions, occasion and job, mining and cellular multimedia purposes, seek, summarization and visualization, visualization and complicated multimedia structures, and the specific classes: interactive and immersive leisure and conversation, multimedia renovation: the right 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
The second pass is applied on clusters generated by the first pass on all objects of the database. To represent a cluster, we use the following definition of the median: argmin ∑ (9) With V – a cluster and vi – members of a cluster, G the candidate median and is a distance or dissimilarity measure in our case. For the first pass, the dissimilarities between all the features, of the same layer, extracted on all the images of an object are computed. For the second pass, only the dissimilarities between all the medians of all object clusters are computed.
Unsupervised discovery of visual object class hierarchies. In: CVPR (2008) 23. : Content-based image retrieval at the end of the early years. IEEE PAMI, 22 (2000) 24. : Ontology-enriched semantic space for video search. In: MULTIMEDIA, pp. 981–990 (2007) 25. : Flickr distance. In: MM (2008) 26. : I2t: Image parsing to text description. Proceedings of IEEE (2009) Combining Image-Level and Segment-Level Models for Automatic Annotation Daniel Kuettel, Matthieu Guillaumin, and Vittorio Ferrari Computer Vision Laboratory, ETH Zurich, Switzerland Abstract.
Average precision of ﬂat and hierarchical classiﬁcation on Pascal VOC concepts 14 H. Bannour and C. Hudelot
(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.