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 foreign convention for researchersand practitioners to percentage their new principles, unique study effects and useful improvement studies from all multimedia similar components. It was once an outstanding honor to have MMM2008, essentially the most long-standing m- timedia meetings, at probably the most appealing and traditionally vital eastern towns. Kyoto used to be an historic capital of Japan, and was once 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 you could get pleasure from outdated shrines and temples that are exact as international history websites. The convention venue was once the Clock Tower Centennial corridor in Kyoto collage, that's one of many oldest universities in Japan. MMM2008 featured a complete software together with 3 keynote talks, six oral presentation classes, and poster and demo classes. The 133 s- missions integrated quite a few fine quality papers in multimedia content material research, multimedia sign processing and communications, and multimedia functions and prone. We thank our 137 Technical application Committee contributors and reviewers who spent many hours reviewing papers and prov- ing invaluable suggestions to the authors. in accordance with the three or four studies in keeping with paper this system Chairs made up our minds to simply accept simply 23 as oral papers and 24 as poster papers, the place each one kind of presentation may well furthermore current the paintings as a demo. The reputation cost of 36% follows the MMM culture of ful?lling fruitful discussions in the course of the conference.

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

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On the other hand, the part-based bag of visual words approach is more robust, as it describes an image based on the statistics of local region descriptors (visual words). One disadvantage of bag-of-words approach is that it neglects the geometric and spatial inter-relation between visual words. To address this issue, Zheng el at. [3] proposed to represent images in terms of visual phrases, which are pairs (bigram) of spatially adjacent visual words. Nevertheless, all of the aforementioned image representations are isolated low level visual features and usually are too primitive to model the images semantics, as their effectiveness depends highly on the visual similarity and regularity of objects of same class.

On the other hand, the part-based bag of visual words approach is more robust, as it describes an image based on the statistics of local region descriptors (visual words). One disadvantage of bag-of-words approach is that it neglects the geometric and spatial inter-relation between visual words. To address this issue, Zheng el at. [3] proposed to represent images in terms of visual phrases, which are pairs (bigram) of spatially adjacent visual words. Nevertheless, all of the aforementioned image representations are isolated low level visual features and usually are too primitive to model the images semantics, as their effectiveness depends highly on the visual similarity and regularity of objects of same class.

To tackle MLMI learning task, we propose a novel algorithm named MILC2 . In MILC2 , an additional constraint penalizing the disagreement of inter-layer predictions is explicitly introduced to fit the MI relation. With this explicit constraint, MILC2 solves the ambiguity propagation problem, which cannot be actually handled in MLMI kernel approach. The experimental results on TRECVID 2005 benchmark have shown that by leveraging the inter-layer consistency constraint, MILC2 achieves better performance than standard SVM based approaches and the MLMI kernel method.

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