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

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 college, Kyoto, Japan. MMM is a number one foreign convention for researchersand practitioners to proportion their new principles, unique learn effects and sensible improvement reports from all multimedia similar components. It used to be a superb honor to have MMM2008, some of the most long-standing m- timedia meetings, at some of the most appealing and traditionally vital eastern towns. Kyoto was once an historic 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 you could get pleasure from outdated shrines and temples that are distinct as global background websites. The convention venue used to be the Clock Tower Centennial corridor in Kyoto college, that is one of many oldest universities in Japan. MMM2008 featured a finished software together with 3 keynote talks, six oral presentation classes, and poster and demo periods. The 133 s- missions incorporated lots of top quality papers in multimedia content material research, multimedia sign processing and communications, and multimedia functions and providers. We thank our 137 Technical application Committee contributors and reviewers who spent many hours reviewing papers and prov- ing necessary suggestions to the authors. in response to the three or four experiences according to paper this system Chairs made up our minds to simply accept in basic terms 23 as oral papers and 24 as poster papers, the place every one form of presentation may well moreover current the paintings as a demo. The attractiveness price of 36% follows the MMM culture of ful?lling fruitful discussions during the conference.

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

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Keywords: image retrieval and representation. Bag of visual synsets. 1 Introduction As the amount of digital images has been increasing explosively, the image retrieval based on their visual content has spurted much research attention. Here, we narrow down our focus to object-based image retrieval (OBIR), which aims to retrieve images l containing salient object of same semantic class c as the given example query image q from a image collection L = {li }in=1 of semantic classes C = {ci }im=1 . Recently, the region-based representation for image retrieval [1-3, 10] has shown much superiority over traditional global features, as one single image feature computed over the entire image are not sufficient to represent important local characteristics of objects.

In: Proceedings of ACM international conference on Multimedia, Santa Barbara, CA, USA, pp. 77–80 (2006) Object-Based Image Retrieval Beyond Visual Appearances 23 4. : Efficient and effective querying by image content. Journal of Intelligent Information Systems 3(3-4), 231–262 (1994) 5. : Visual information retrieval. Communications of the ACM 40(5), 70–79 (1997) 6. : VisualSEEk: a fully automated content-based image query system. S, pp. 87–98 (November 1996) 7. : SIMPLIcity: Semantics-Sensitive Integrated Matching for Picture Libraries.

Fig. 3. 1 Information Bottleneck (IB) Principle By formulating visual synset construction as a task of visual word clustering based on their object class probability distributions, the issue now is reduced to how to measure the ’right’ distance between these distributions, namely the similarity metric in clustering. The Information Bottleneck (IB) principle [12][13] provides a reasonable solution to address this issue. Given the joint distribution P(w, c) of the visual words w and image classes c, the goal of IB principle is to construct the optimal compact representation of w, namely the visual synset clusters s, such that s preserves as much information about c as possible.

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