By Alan Hanjalic (auth.), Klaus Schoeffmann, Bernard Merialdo, Alexander G. Hauptmann, Chong-Wah Ngo, Yiannis Andreopoulos, Christian Breiteneder (eds.)
This ebook constitutes the refereed complaints of the 18th foreign Multimedia Modeling convention, MMM 2012, held in Klagenfurt, Austria, in January 2012. The 38 revised common papers, 12 certain 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 job, mining and cellular multimedia functions, seek, summarization and visualization, visualization and complicated multimedia structures, and the specific classes: interactive and immersive leisure and communique, multimedia protection: easy methods to be sure multimedia entry through the years, multi-modal and cross-modal seek, and video surveillance.
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Additional info for Advances in Multimedia Modeling: 18th International Conference, MMM 2012, Klagenfurt, Austria, January 4-6, 2012. Proceedings
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 ). 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 have proposed new models for some of the stages and, importantly, novel models to perform joint prediction on both levels.
The steps are repeated until convergence. 3 Label-To-Region (LTR) This is the approach described in the recent work of . It consists of two stages. First, corresponding segments between image with common labels are found. Second, labels are assigned to segments based on these correspondences. In the first stage, a segment s in an image Ii is approximated in the feature space as a sparse linear combination of segments s ∈ S in other images I\Ii sharing at least one label. Then, labels are transferred to s from S according to the sparse linear combination.
Ca/research/data/eccv_2002/ 26 D. Kuettel, M. Guillaumin, and V. Ferrari Table 2. Pixel annotation results on the SIFT-Flow dataset Name (Parameters) Overall acc. 9% Table 3. Image annotation results on the Corel5k dataset. TagProp is abbreviated as TP and SegProp as SP. Name (Parameters) Token Model (Q = 2300) SP (Q = 2300, K = 50) SP (K = 50) Global SP (R = 10, K = 5) TP (K = 200) TP+Token TP+SP TP+Global SP (K = 200, R = 10, T = 5) A TP TP TP - B Token Token Copy Copy Token Copy Copy C D E Token Max.
Advances in Multimedia Modeling: 18th International Conference, MMM 2012, Klagenfurt, Austria, January 4-6, 2012. Proceedings by Alan Hanjalic (auth.), Klaus Schoeffmann, Bernard Merialdo, Alexander G. Hauptmann, Chong-Wah Ngo, Yiannis Andreopoulos, Christian Breiteneder (eds.)