Download Advances in Multimedia Modeling: 14th International by Yuli Gao, Jianping Fan, Hangzai Luo, Shin’ichi Satoh PDF

By Yuli Gao, Jianping Fan, Hangzai Luo, Shin’ichi Satoh (auth.), Shin’ichi Satoh, Frank Nack, Minoru Etoh (eds.)

Welcometothe14thInternationalMultimediaModelingConference(MMM2008), held January 9–11, 2008 at Kyoto collage, Kyoto, Japan. MMM is a number one overseas convention for researchersand practitioners to proportion their new rules, unique learn effects and sensible improvement reports from all multimedia comparable components. It used to be a good honor to have MMM2008, some of the most long-standing m- timedia meetings, at the most appealing and traditionally vital jap towns. Kyoto was once an old capital of Japan, and used to be and nonetheless is on the heartofJapanesecultureandhistory. Kyotoinwintermaydistinctivelyo?er the sober surroundings of an ink portray. you could get pleasure from outdated shrines and temples that are specified as global background websites. The convention venue was once the Clock Tower Centennial corridor in Kyoto college, that's one of many oldest universities in Japan. MMM2008 featured a entire application together with 3 keynote talks, six oral presentation periods, and poster and demo periods. The 133 s- missions integrated numerous fine quality papers in multimedia content material research, multimedia sign processing and communications, and multimedia purposes and providers. We thank our 137 Technical application Committee contributors and reviewers who spent many hours reviewing papers and prov- ing beneficial suggestions to the authors. according to the three or four reports in line with paper this system Chairs made up our minds to just accept in simple terms 23 as oral papers and 24 as poster papers, the place each one kind of presentation may possibly furthermore current the paintings as a demo. The reputation expense of 36% follows the MMM culture of ful?lling fruitful discussions during the conference.

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Extra resources for Advances in Multimedia Modeling: 14th International Multimedia Modeling Conference, MMM 2008, Kyoto, Japan, January 9-11, 2008. Proceedings

Sample text

Due to multi-layer extension, the ambiguity in MI setting will propagate layer by layer, we define such phenomena as ambiguity propagation, and appropriate modeling the MI relation is of vital importance addressing the problem. In this paper, we propose a novel algorithm named MILC2 1 to explicitly model the multi-instance relations through layers and solve the ambiguity propagation problem. MILC2 tackles the MLMI learning problem by reformulating the objective function with an explicit inter-layer consistency constraint.

S. Satoh, F. Nack, and M. ): MMM 2008, LNCS 4903, pp. 24–34, 2008. c Springer-Verlag Berlin Heidelberg 2008 MILC2 : A Multi-Layer Multi-Instance Learning Approach 25 We consider video as a multi-layer multi-instance (MLMI) setting [5]. An ideal concept detector should model both the multi-layer structure and the inherent multi-instance relation simultaneously. By multi-layer (ML), we indicate that video is constructed by multiple layer information, for example in Figure 1, a shot is composed of shot, key-frame and region layers.

Gov/projects/TRECVID 2. : MILES: Multiple-instance learning via embedded instance selection. IEEE Trans. on Pattern Analysis and Machine Intelligence 28(12), 1931–1947 (2006) 34 Z. Gu et al. 3. : A regularization framework for multiple-instance learning. In: Proceedings of International Conference on Machine Learning, pp. 193–200. ACM Press, New York (2006) 4. : Unsupervised segmentation of color-texture regions in images and video. IEEE Trans. on Pattern Analysis and Machine Intelligence 23(8), 800–810 (2001) 5.

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