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

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

This e-book constitutes the refereed complaints of the 18th foreign Multimedia Modeling convention, MMM 2012, held in Klagenfurt, Austria, in January 2012. The 38 revised standard papers, 12 distinctive consultation papers, 15 poster consultation papers, and six demo consultation papers have been rigorously reviewed and chosen from 142 submissions. The papers are equipped 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 periods: interactive and immersive leisure and conversation, multimedia protection: the best way to make certain multimedia entry over the years, multi-modal and cross-modal seek, and video surveillance.

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Label predictions using SegProp and TagProp+SegProp (top row), Token and TagProp+Token respectively (bottom row), are shown. In both cases, the combined method improves over the segment-level one. 2%). Importantly, it also improves over state-of-the-art TagProp alone. Therefore, our method also improves over other works such as [9,13], which were outperformed by TagProp (see scores for MBRM or TGLM within [11]). 10 Conclusion We have presented a unified view on image-level and segment-level methods, where existing works can be casted in a common framework.

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.

SVRC allows estimating the semanticovisual relatedness of concepts. A new heuristic, TRUST-ME, is also proposed for reasoning about concepts relatedness, and to link together concepts that are semantically related in a semantic hierarchy. Our experiments showed that the built semantic hierarchy improves significantly the classification performance on Pascal VOC dataset. Our future research will concern the evaluation of our approach on larger datasets (MirFlicker and ImageNet), and the assessment of our hierarchy in terms of structure and contribution of knowledge.

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