Download Advances in Multimedia Information Processing – PCM 2015: by Yo-Sung Ho, Jitao Sang, Yong Man Ro, Junmo Kim, Fei Wu PDF

By Yo-Sung Ho, Jitao Sang, Yong Man Ro, Junmo Kim, Fei Wu

The two-volume lawsuits LNCS 9314 and 9315, represent the complaints of the 16th Pacific-Rim convention on Multimedia, PCM 2015, held in Gwangju, South Korea, in September 2015.

The overall of 138 complete and 32 brief papers awarded in those complaints used to be rigorously reviewed and chosen from 224 submissions. The papers have been equipped in topical sections named: photograph and audio processing; multimedia content material research; multimedia purposes and companies; video coding and processing; multimedia illustration studying; visible realizing and popularity on substantial info; coding and reconstruction of multimedia information with spatial-temporal info; 3D image/video processing and functions; video/image caliber evaluation and processing; social media computing; human motion reputation in social robotics and video surveillance; fresh advances in image/video processing; new media illustration and transmission applied sciences for rising UHD companies.

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Additional info for Advances in Multimedia Information Processing – PCM 2015: 16th Pacific-Rim Conference on Multimedia Gwangju, South Korea, September 16–18, 2015, Proceedings, Part I

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Greedy subspace clustering methods provide an efficient way to cluster large-scale multimedia datasets. However, these methods do not guarantee a global optimum and their clustering performance mainly depends on their initializations. To alleviate this initialization problem, this paper proposes a two-step greedy strategy by exploring proper neighbors that span an initial subspace. Firstly, for each data point, we seek a sparse representation with respect to its nearest neighbors. The data points corresponding to nonzero entries in the learning representation form an initial subspace, which potentially rejects bad or redundant data points.

It is based on the assumption that high-dimensional data often lies on lowdimensional subspaces, which usually holds true for the data acquired in real world. Subspace clustering can be widely applied in image segmentation [1], motion segmentation [2], face clustering [3], image representation, compression [4], and multimedia analysis [5,6]. , pixels belong to the same object, feature points of the same rigid object in a moving video sequence, face images of the same person) lie on same underlying subspace, and the mixture dataset can be modeled by unions of subspaces.

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