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 examine effects and sensible improvement studies from all multimedia comparable parts. It was once an excellent honor to have MMM2008, essentially the most long-standing m- timedia meetings, at some of the most appealing and traditionally very important jap towns. Kyoto was once 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 you could take pleasure in outdated shrines and temples that are detailed as global background websites.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 classes, and poster and demo periods. The 133 s- missions integrated various top quality papers in multimedia content material research, multimedia sign processing and communications, and multimedia functions and companies. We thank our 137 Technical software Committee contributors and reviewers who spent many hours reviewing papers and prov- ing worthy suggestions to the authors. in keeping with the three or four experiences consistent with paper this system Chairs determined to simply accept simply 23 as oral papers and 24 as poster papers, the place every one form of presentation may possibly moreover current the paintings as a demo. The reputation fee 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
To tackle the ambiguity propagation problem, in which an inter-layer consistency constraint is explicitly introduced to measure the disagreement of inter-layers, and thus the MI relation is better modeled. This learning task is formulated in a regularization framework with three components including hyper-bag prediction error, inter-layer inconsistency measure, and classiﬁer complexity. We apply the proposed MILC2 to video concept detection over TRECVID 2005 development corpus, and report better performance than both standard Support Vector Machine based and MLMI kernel methods.
Section 2 gives the proposed algorithm, including the MLMI kernel and the MILC2 approach. Section 3 provides the implementation issues for solving MILC2 by CCCP. Section 4 presents the experimental results. Finally, concludes in Section 5 with some outlooks in the future. 1 The Proposed MILC2 Algorithm A MLMI Kernel In MLMI setting, each sample can be viewed as an L-layer rooted tree. A rooted tree is a connected acyclic directed graph in which each node is connected by a MILC2 : A Multi-Layer Multi-Instance Learning Approach 27 unique path from a designated node called the root.
N 1 , l = 2, . . , L; However, we cannot solve Eq. (12) directly since W lies in the mapped feature space which usually goes inﬁnite. Instead, we get rid of the explicit usage of W by forming the dual optimization problem. We introduce Lagrange multipliers ∗2 L L ∗L ∗L T α = α11 , . . , α1N 1 , α21 , . . , α2N 1 , α∗2 in the 1 , . . , αN 1 , α1 , . . , αN 1 , α1 , . . t. Y T α = 0; 0 ≤ α ≤ Λ. (13) MILC2 : A Multi-Layer Multi-Instance Learning Approach 31 N and the equality W = i=1 (Aα)i × φ(I(i)) in which, α, p, Y, Λ are M = (2L − 1)N 1 dimensional vectors, with entrances pi = Λi = −1 1 ≤ i ≤ N 1 ; 0 otherwise Yi = yi 1 ≤ i ≤ N 1 ; 0 otherwise; λ1 1 ≤ i ≤ N 1 ; λl (2l − 3)N 1 + 1 ≤ i ≤ (2l − 1)N l ; l = 2, .