Rushes summarization using different redundancy elimination approaches

ABSTRACT

Generating short summary videos for rushes is a challenging task due to the difficulty in eliminating redundancy and determining the important objects and events to be placed in the summary. Redundancy elimination is difficult since repetitive segments, which are takes of the same scene, usually have different lengths and motion patterns. This makes approaches using one keyframe for a shot representation fail when doing clustering. In addition, even repetitive segments can be precisely determined, but the summary generated by concatenating together the selected segments still takes longer than the upper limit. Selecting a sub-segment that conveys as much of the information concerning a given scene as possible might be a good way to improve this process. We introduce two approaches to solve these problems. In the first approach, one keyframe is used for representing a shot when doing clustering; and sub-segments are selected using the motion information for generating the summary. Meanwhile, in the second approach, all the frames of a given shot are used for clustering; and a simple skimming method is used to select the sub-segments. The experimental results on the TRECVID 2008 dataset and a comparison between the two approaches are also reported.

REFERENCES

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INDEX TERMS

Primary Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.1 Content Analysis and Indexing
          Subjects: Indexing methods

Additional Classification:
  I. Computing Methodologies
  I.5 PATTERN RECOGNITION
      I.5.3 Clustering
          Subjects: Algorithms

General Terms:
Algorithms, Experimentation, Performance

Keywords:
greedyrsc clustering, rushes content, video skimming, video summarization