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The 2 quantity set LNCS 4351 and LNCS 4352 constitutes the refereed lawsuits of the thirteenth foreign Multimedia Modeling convention, MMM 2007, held in Singapore in January 2007.Based on rigorous reviewing, this system committee chosen 123 rigorously revised complete papers of the most technical classes and 33 revised complete papers of four designated periods from a complete of 392 submissions for presentation in volumes. the 1st quantity comprises seventy six contributions regarding studying semantic recommendations, pix, picture registration, matching and texture, human-computer interplay, monitoring and movement research, complicated media coding and variation, media annotation, photo and video coding, context-aware media modeling, multimedia databases, media retrieval, P2P networks, semantic video ideas, audio and video coding, content material, functions, desktop imaginative and prescient, and snapshot processing. the second one quantity collects eighty papers on the topic of multimedia sign processing and communications, occasion detection, multimedia over P2P, content material, functions, desktop imaginative and prescient, picture processing, multimedia sign processing and communications, photograph class and popularity, complicated media processing and safety, ubiquitous multimedia provider (UMS), companies and the reassurance in multimedia cellular info structures (SAMM) , networked mulitmedia platforms and its functions targeting trustworthy and versatile supply for built-in multimedia (NMS), and networked pix appications (NGA).
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Additional info for Advances in Multimedia Modeling: 13th International Multimedia Modeling Conference, MMM 2007, Singapore, January 9-12, 2007. Proceedings, Part I
In accord to the 20 Y. -S. Chua, and R. Jain decision tree, the framework employs a single expert that annotates the image clusters with one of the two mutually exclusive concepts. To facilitate feature selection, we calculate class weighted feature scores for periods using the method discussed in Section 3. 2. The framework utilizes feature scores during the model selection step as described in Section 3. 3. Second, we back-project clusters onto their respective paintings and employ the majority vote technique to annotate the art period concept to the whole painting.
The expert at the i-th level has the input vector (X,Si-1) received from the ancestor node and generates the output vector Si, where X represents a pattern. Si represents the set of classes to which the expert of i-th level believes the pattern X might belong and the set Si is a subset of its respective set Si-1 (Sn⊂ Sn-1 ⊂ Si …⊂ S0). During the annotation process, if the terminal node is reached, then the unlabelled patterns under this node are labeled with a single element of Si. Table 1. Visual-level brushwork concepts Class Characteristics Shading Depiction of foldings in Medieval Period Glazing Depiction of nudity/face in Medieval Period Background Edges and gradients, often directional, intensity contrast, weakly or non-homogeneous Subset of hues (yellow, red, orange), intensity contrast, gradients, non-homogeneous, may contain edges Examples Widely used technique in paintings.
How to learn more visual hints from the user strokes on the image is a challenging problem. Only using the color information is not enough to model the background and foreground which have similar colors. In the future, we will add the texture information to analyze this problem. For another future work, we will compare our method with the multiway min-cut algorithm . Acknowledgements This work is supported by the Projection (60475001) of the National Nature Science Foundation of China. References 1.