By Sun-Yuan Kung (auth.), Paisarn Muneesawang, Feng Wu, Itsuo Kumazawa, Athikom Roeksabutr, Mark Liao, Xiaoou Tang (eds.)
This ebook constitutes the court cases of the tenth Pacific Rim convention on Multimedia, held in Bangkok, Thailand in the course of December 15-18, 2009.
The papers offered within the quantity have been conscientiously reviewed and chosen from 171 submissions. the themes coated are exploring large-scale videos:automatic content material style category, fix, enhancement and authentication, human habit type and popularity, photograph and video coding perceptual caliber development, photo annotation, retrieval, and type, item detection and monitoring, networking applied sciences, audio processing, 3DTV and mulit-view video, photo watermarking, multimedia record seek and retrieval, clever multimedia defense and forensics, multimedia content material administration, photo research and matching, coding, complex photo processing ideas, multimedia compressioin and optimization, multimedia protection rights and management.
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Additional resources for Advances in Multimedia Information Processing - PCM 2009: 10th Pacific Rim Conference on Multimedia, Bangkok, Thailand, December 15-18, 2009 Proceedings
C. edu Abstract. With a rapidly dropping price in hand-held cameras and video editing software, user-generated contents are popular these days, especially on online video sharing websites. To facilitate eﬃcient management of large video collections, it is essential to be able to separate amateur video contents from professional ones automatically. In this work, we propose several features that take into account the camera operation and the nature of amateur video clips to achieve this goal. In the proposed scheme, we estimate the number of diﬀerent cameras being used in a short time interval, the shakiness of the camera, and the distance between the camera and the subject.
Equivalence property among the three Hilbert spaces. For supervised classiﬁcation, the three spaces are basically interchangeable. However, the tie between the basic kernel space and the spectral spaces is even stronger, since the distance of any pair of the training objects are preserved between these two spaces. 2 Equivalence Property for Unsupervised Clustering For distance-based unsupervised clustering, the focus of attention is on the equivalence of the distance metric in diﬀerent spaces. As depicted in Figure 6, while the two spaces E and K are tightly related by a linear mapping, are mutually exchangeable for applications related to supervised classiﬁcation.
Subject to no constraint (with a direct solution: a = [K + ρI]−1 y); or 2. subject to OHP constraint: aT e = 0; or 3. subject to OHP and additional lower/upper bounds on ai . e. +baT e ), which leads to a new cost function: 1 aT y − aT [K + ρI]a + baT e. rt. a, the optimal solution can be found: a = [K + ρI]−1 [y − be], where b = yT [K + ρI)]−1 e . eT [K + ρI)]−1 e (76) This is hardly a new solution. e. the solution is noise-free. – When ρ > 0, this leads to the PDA solution which is is no longer noise-free.