By Enqing Chen, Yihong Gong, Yun Tie

ISBN-10: 3319488899

ISBN-13: 9783319488899

ISBN-10: 3319488902

ISBN-13: 9783319488905

The two-volume complaints 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 offered in those lawsuits was once rigorously reviewed and chosen from 202 submissions.

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

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Read Online or Download Advances in Multimedia Information Processing - PCM 2016: 17th Pacific-Rim Conference on Multimedia, Xi´ an, China, September 15-16, 2016, Proceedings, Part I PDF

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

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28, 1–10 (2009). 124 5. : Color filter for image search. In: Proceedings of ACM Multimedia, pp. 1327–1328 (2012) 6. : Saliency detection via absorbing Markov chain. In: ICCV, pp. 1665–1672 (2013) 7. : SLIC Superpixels, Technical report (2010) 8. : Saliency detection via graph-based manifold ranking. In: CVPR, pp. 3166–3173 (2013) 9. : Saliency detection with multi-scale superpixels. IEEE Sig. Process. Lett. 21, 1035–1039 (2014) 10. : Saliency detection via cellular automata. In: CVPR, pp. 110–119 (2015) 11.

2 Implementation Detail We implement the proposed method based on the open source Caffe [7] framework. 05 W padding on all sides (top, bottom, left, right), where W is the width of the bounding box. Then we resize the cropped image to 224 × 224. Facial Landmark Localization by Part-Aware Deep Convolutional Network 27 We use the pre-trained VGG-S [4] model to initialize our network. The first four convolutional layers of the VGG-S network are used to initialize four shared convolutional layers. These shared layers are designed to produce feature maps from the entire input image.

Robust visual tracking via structured multi-task sparse learning. Int. J. Comput. Vis. (IJCV) 101, 367–383 (2013) 23. : Robust object tracking via sparsity-based collaborative model. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. cn Abstract. In this paper, we propose an improved mechanism for saliency detection. Firstly, based on a neoteric background prior selecting four corners of an image as background, color and spatial contrast with each super-pixel are being used to obtain a coarse map.

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Advances in Multimedia Information Processing - PCM 2016: 17th Pacific-Rim Conference on Multimedia, Xi´ an, China, September 15-16, 2016, Proceedings, Part I by Enqing Chen, Yihong Gong, Yun Tie


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