Advances in Multimedia Information Processing - PCM 2016: by Enqing Chen, Yihong Gong, Yun Tie

By Enqing Chen, Yihong Gong, Yun Tie

The two-volume court cases LNCS 9916 and 9917, represent the court cases of the 17th Pacific-Rim convention on Multimedia, PCM 2016, held in Xi`an, China, in September 2016.

The overall of 128 papers awarded in those court cases was once rigorously reviewed and chosen from 202 submissions.

The concentration of the convention used to be as follows in multimedia content material research, multimedia sign processing and communications, and multimedia purposes and services.

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Extra info for Advances in Multimedia Information Processing - PCM 2016: 17th Pacific-Rim Conference on Multimedia, Xi´ an, China, September 15-16, 2016, Proceedings, Part II

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When seeing an amazing view, people always appear struggling in creating satisfactory photographs. In this paper, we propose to solve the problem from the specific perspective of layout recommendation. As shown in Fig. 1, given a scene as background, our method supplies professional advice for people’s layout. The final photographs following our guidance get better aesthetic feelings. For example, the left image shows a best visual balance of the scene elements. The middle image shows a good feeling of symmetry.

Then we employ a 3D layout aesthetic estimation model to rank the proposed photos. The results with high scores are selected as layout recommendations, which is then translated to a hint for where people shall locate. The key to our success lies on the combination of 3D structures with aesthetic models. The subjective evaluation shows superior preference of our method to previous work. We also give a few application examples to show the power of our method in creating better daily life photographs.

Finally, the proof of the convergence of the proposed algorithm is given. 1 Formulation of the Optimization Problem Given the magnitude spectrum of mixtures V, we assume that V is the superposition of a low-rank repeating background magnitude WH and a sparse varied foreground S. 26 Y. Li et al. The goal of RNMF is to minimize the divergence between the non-negative matrix V (magnitude of the mixtures) and its approximation WH + S, with all the matrices constrained to be non-negative. Our previous study [14] has shown that by imposing a threshold, which can be seen as an element-wise Boole operation, a sparse estimation of S will be obtained.

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