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

By Enqing Chen, Yihong Gong, Yun Tie

ISBN-10: 3319488899

ISBN-13: 9783319488899

ISBN-10: 3319488902

ISBN-13: 9783319488905

The two-volume lawsuits 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 provided in those lawsuits used to be 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 functions and services.

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Read 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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Our method achieves better performance in terms of different evaluation metrics, compared with the state-of-arts on three benchmark image datasets. Acknowledgement. This work was supported by the Key Science and Technology Planning Project of Hunan Province, China (2014GK2007). Saliency Detection Combining Multi-layer Integration Algorithm 21 References 1. : Context-aware saliency detection. In: CVPR, pp. 2376–2383 (2010) 2. : Is bottom-up attention useful for object recognition? In: CVPR, pp. 37–44 (2004) 3.

A) Input image; (b) Corner prior map; (c) Objectness prior map; (d) Combine corner with objectness prior without using energy function. Saliency Detection Combining Multi-layer Integration Algorithm P ep à Wp ¼ W À Á labh ðxp ; yp Þ Â t xp ; yp À Á P xp ;yp labh xp ; yp þ h xp ;yp 15 ð7Þ À Á À Á where labh xp ; yp ¼ 1 indicates that the pixel located at xp ; yp of the input image À Á À Á belongs to the h-th object candidate, and labh xp ; yp ¼ 0 otherwise; xp ; yp 2 ½0; 1Š represents the CBSP value of pixel.

4. The activation of sub-network. The first column is the input image. The second column is the activation of contour sub-network conv5 layer, high activation parts are around corner. The third column is the activation of inner sub-network conv5 layer, high activations are corresponding to the eyes and nose parts. ) methods, including PCRR [2], GN-DPM [14], CFAN [17], ESR [3], SDM [15], ERT [8], LBF [11]. The overall experimental results are reported in Table 3. We improve the performance on the 300-W dataset, especially on Challenging test set.

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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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