MV-MAP: Multiresolution video visualization and summarization on MAPs

Yingge Wang, Qiang Cheng, Jie Cheng, Thomas S. Huang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper considers visualizing and summarizing image sequences using manifold learning and multiresolution techniques. The images in a video are found usually lying on a significantly low-dimensional manifold, which provides intrinsic information on the video content and formation. The parametrization of the manifold is discovered using a nonlinear subspace method preserving underlying geometry, especially local topology, in the original space. Two modes of video roadmaps have been constructed using VMAPs. The first discovers the landmark points signaling dramatic changes in video content in the temporal order. The second reveals the global content coherence, without the temporal ordering. To facilitate the browsing of long sequences with complicated contents and structures, we build multiresolution visualization and summarization tools on VMAPs. Experimental results validate the proposed method. It may find applications to video monitoring and surveillance for interactive exploitation of video contents, intrusion detection, etc.

Original languageEnglish (US)
Title of host publicationProceedings of the 17th International Conference on Pattern Recognition, ICPR 2004
EditorsJ. Kittler, M. Petrou, M. Nixon
Pages886-889
Number of pages4
DOIs
StatePublished - Dec 20 2004
EventProceedings of the 17th International Conference on Pattern Recognition, ICPR 2004 - Cambridge, United Kingdom
Duration: Aug 23 2004Aug 26 2004

Publication series

NameProceedings - International Conference on Pattern Recognition
Volume3
ISSN (Print)1051-4651

Other

OtherProceedings of the 17th International Conference on Pattern Recognition, ICPR 2004
Country/TerritoryUnited Kingdom
CityCambridge
Period8/23/048/26/04

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition

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