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

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 used to be as follows in multimedia content material research, multimedia sign processing and communications, and multimedia purposes 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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Additional 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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Springer, Heidelberg (2012). 1007/978-3-642-33712-3_3 16. : Saliency filters: contrast based filtering for salient region detection. In: CVPR, pp. 733–740 (2012) 17. : Graph-regularized saliency detection with convex-hull-based center prior. IEEE Sig. Process. Lett. 20, 1608–1623 (2013) 18. : What makes a patch distinct? In: CVPR, pp. 1139– 1146 (2013) 19. : Minimum barrier salient object detection at 80 FPS. In: ICCV (2015) 20. : Image segmentation by probabilistic bottom-up aggregation and cue integration.

1007/978-3-319-48890-5 4 On Combining Compressed Sensing and Sparse Representations 33 To deal with with the appearance transformations, Comanicu [3] integrated the trained SVM classifier into optical flow framework for tracking. In reference [4], a collection of online-learning weak classifier was used to distinguish whether the pixel belongs to the target or the background. Another method adopted is multiple-instance learning whose main idea is to package all ambiguous positive and negative samples and then learn the distinguishing model on-line [5].

Accordingly, m-1 increasing ∧ point sets q will be generated in the interval of [0,1]. ∧ q= 2n − 1 , 2m + 1 n ∈ [1, m − 1] (11) By m-1 points, the interval [0,1] is divided into m subintervals increasing in length. In dictionary D, there are m target templates that corresponds to the m subintervals. Every time in updating, there will be a random number r in interval [0,1]. The template corresponding to the interval which r falls into will be eliminated and then a new template will be added to the end of the template 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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