NONLINEAR KERNEL SPARSE DICTIONARY SELECTION FOR VIDEO SUMMARIZATION

Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 32 - Refereed conference paper (with host publication)peer-review

10 Scopus Citations
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Author(s)

  • Mingyang Ma
  • Shaohui Mei
  • Shuai Wan
  • Zhiyong Wang
  • Dagan Feng

Related Research Unit(s)

Detail(s)

Original languageEnglish
Title of host publicationProceedings of the IEEE International Conference on Multimedia and Expo (ICME) 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages637-642
ISBN (print)9781509060672
Publication statusPublished - Jul 2017

Conference

TitleIEEE International Conference on Multimedia and Expo (ICME) 2017
LocationHarbour Grand Kowloon Hotel
PlaceHong Kong
Period10 - 14 July 2017

Abstract

Sparse dictionary selection (SDS) has demonstrated to be an effective solution for keyframe based video summarization (VS), which generally assumes a linear relation among similar video frames. However, such a linear assumption is not always true for videos. In this paper, the nonlinearity among frames is taken into consideration and a nonlinear SDS model is formulated for VS, in which the nonlinearity is transformed to linearity by projecting a video to a high dimensional feature space induced by a kernel function. Moreover, a kernel simultaneous orthogonal matching pursuit (KSOMP) is proposed to solve the problem. In order to achieve an intuitive and flexible configuration of the VS process, an adaptive criterion is devised to produce video summaries with different lengths for different video content. Experimental results on benchmark video datasets demonstrate that the proposed algorithm outperforms several state-of-the-art VS algorithms.

Research Area(s)

  • sparse dictionary selection, video summarization, keyframe extraction, simultaneous orthogonal matching pursuit (SOMP), nonlinear

Citation Format(s)

NONLINEAR KERNEL SPARSE DICTIONARY SELECTION FOR VIDEO SUMMARIZATION. / Ma, Mingyang; Mei, Shaohui; Hou, Junhui et al.
Proceedings of the IEEE International Conference on Multimedia and Expo (ICME) 2017. Institute of Electrical and Electronics Engineers Inc., 2017. p. 637-642 8019387.

Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 32 - Refereed conference paper (with host publication)peer-review