Download Advances in Multimedia Information Processing – PCM 2012: by Chao Wang, Yunhong Wang, Zhaoxiang Zhang (auth.), Weisi Lin, PDF

By Chao Wang, Yunhong Wang, Zhaoxiang Zhang (auth.), Weisi Lin, Dong Xu, Anthony Ho, Jianxin Wu, Ying He, Jianfei Cai, Mohan Kankanhalli, Ming-Ting Sun (eds.)

ISBN-10: 3642347770

ISBN-13: 9783642347771

This publication constitutes the court cases of the thirteenth Pacific Rim convention on Multimedia, held in Singapore in the course of December 4-6, 2012. The fifty nine revised complete papers provided have been conscientiously reviewed and chosen from 106 submissions for the most convention and are observed through 23 displays of four distinct classes. The papers are geared up in topical sections on multimedia content material research, photograph and video processing, video coding and multimedia details processing, image/video processing and research, video coding and multimedia process, complicated picture and video coding, go media studying with structural priors, in addition to effective multimedia research and utilization.

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Advances in Multimedia Information Processing – PCM 2012: 13th Pacific-Rim Conference on Multimedia, Singapore, December 4-6, 2012. Proceedings

This booklet constitutes the lawsuits of the thirteenth Pacific Rim convention on Multimedia, held in Singapore in the course of December 4-6, 2012. The fifty nine revised complete papers offered have been conscientiously reviewed and chosen from 106 submissions for the most convention and are followed by way of 23 shows of four targeted periods.

Extra resources for Advances in Multimedia Information Processing – PCM 2012: 13th Pacific-Rim Conference on Multimedia, Singapore, December 4-6, 2012. Proceedings

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1. Segment-based approach for motion features Essentially, segment-based approach differs from the adaptive keyframe-based approach in three aspects: (1) trajectory features are extracted from a denser volume; (2) motion features captured in the a whole segment are more reliable (regularly, motion information at a specific keyframe is either constant or nonconsistent); and (3) combining segment-based motion features with static visual features must be done at video-level, while for the former approach, it can be done at keyframe-level.

Detecting event in multimedia video has become a popular research topic. One of the most important clues to determine an event in video is its motion features. Currently, motion features are often extracted from the whole video using dense sampling strategy. However, this extraction method is computationally prohibitive when it comes to large scale video dataset. Moreover, video length may be very different, which makes it unreliable to compare the feature between videos. In this paper, we propose to use segment-based approach to extract motion feature.

The on-going LSH-related methods, for instance kernerlized LSH [4], and metric learning-based (Mahalanobis distance function) LSH [5], are extended to different sorts of distance metrics. In contrast, the latest algorithm aspires to develop data-related hashing methods that usually yield more compact binary codes with sufficient information, since the data-dependent mode relies on data distribution. There are remarkable instances in data-dependent algorithms, for example, semi-supervised hashing [6], spectral hashing [7], hashing with graphs [8] and semantic hashing [9].

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