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 percentage their new principles, unique study effects and functional improvement reports from all multimedia comparable parts. It was once a superb honor to have MMM2008, probably the most long-standing m- timedia meetings, at probably the most attractive and traditionally very important jap towns. Kyoto used to be an historical 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 previous shrines and temples that are specified as international background websites. The convention venue used to be 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 lots of top of the range papers in multimedia content material research, multimedia sign processing and communications, and multimedia purposes and companies. We thank our 137 Technical application Committee individuals and reviewers who spent many hours reviewing papers and prov- ing beneficial suggestions to the authors. according to the three or four reports in keeping with paper this system Chairs made up our minds to simply accept in simple terms 23 as oral papers and 24 as poster papers, the place every one kind of presentation may 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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Additional info for Advances in Multimedia Modeling: 14th International Multimedia Modeling Conference, MMM 2008, Kyoto, Japan, January 9-11, 2008. Proceedings
SIFT) . This part-based image representation allows for robustness against partial occlusions, clutter and varying object appearances caused by changes in pose, image capturing conditions, scale, translation and so on . Though various systems [1-3, 10] have shown the superiority of part-based image representation, the bag of words approach is still feeble to deliver satisfactory image retrieval. The reason is obvious. As the bag of words image representation is isolated primitive low level visual feature, its retrieval performance relies highly on the visual similarity and regularity of objects of same semantic class.
T. ,N 1 f (Tim ) − f (Ti ) ≤ δli∗ , i = 1, . . , N 1 , l = 2, . . , L; ⎪ ⎪ ⎩ i = 1, . . , N 1 , l = 2, . . , L; 0 ≤ δ1i , 0 ≤ δli , 0 ≤ δli∗ , where δ1 = [δ11 , δ12 , . . , δ1N 1 ]T are slack variables for the prediction errors on ∗ ∗ ∗ T , δl2 , . . , δlN are slack the hyper-bags, δl = [δl1 , δl2 , . . , δlN 1 ]T and δl∗ = [δl1 1] th variables for the prediction inconsistency between l layer and the root layer, and 1 = [1, 1, . . , 1]T is vectors of 1’s. 30 Z. Gu et al. Let f be a linear function in the mapped high dimensional space f (x) = W T φ(x) + b, where φ(·) is the mapping function.
L 2 L λl δlT 1 + δl∗T 1 min ∗ (12) l=2 ⎧ T i = 1, . . , N 1 ; ⎪ ⎪ 1 − yi (W φ(Ti ) 1+ b) ≤ δ1i , ⎪ ⎨ T N l l W φ(Ti ) − m=1 βim W T φ(Tim ) ≤ δli , i = 1, . . , N 1 , l = 2, . . t. 1 N l T l T ⎪ β W φ(Tim ) − W φ(Ti ) ≤ δli∗ , i = 1, . . , N 1 , l = 2, . . , L; ⎪ ⎪ ⎩ m=1 im 0 ≤ δ1i , 0 ≤ δli , 0 ≤ δli∗ , i = 1, . . , 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.