• Corpus ID: 16938596

The Video Genome

@article{Bronstein2010TheVG,
  title={The Video Genome},
  author={Alexander M. Bronstein and Michael M. Bronstein and Ron Kimmel},
  journal={ArXiv},
  year={2010},
  volume={abs/1003.5320}
}
Fast evolution of Internet technologies has led to an explosive growth of video data available in the public domain and created unprecedented challenges in the analysis, organization, management, and control of such content. The problems encountered in video analysis such as identifying a video in a large database (e.g. detecting pirated content in YouTube), putting together video fragments, finding similarities and common ancestry between different versions of a video, have analogous… 

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References

SHOWING 1-10 OF 33 REFERENCES

Efficient representations of video sequences and their applications

Detecting Irregularities in Images and in Video

  • Oren BoimanM. Irani
  • Computer Science
    Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1
  • 2005
This work addresses the problem of detecting irregularities in visual data, e.g., detecting suspicious behaviors in video sequences, or identifying salient patterns in images, using a probabilistic graphical model.

Finishing the euchromatic sequence of the human genome

The near-complete sequence reported here should serve as a firm foundation for biomedical research in the decades ahead and greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death.

Finishing the euchromatic sequence of the human genome

The near-complete sequence reported here should serve as a firm foundation for biomedical research in the decades ahead and greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death.

Robust video signature based on ordinal measure

We propose a video signature based on an ordinal measure of resampled video frames, which is robust to changing compression formats, compression ratios, frame sizes and frame rates. For effective

Video Google: a text retrieval approach to object matching in videos

We describe an approach to object and scene retrieval which searches for and localizes all the occurrences of a user outlined object in a video. The object is represented by a set of viewpoint

Spatio-temporal features for robust content-based video copy detection

A new method for robust content-based video copy detection based on local spatio-temporal features as shown by experimental validation brings additional robustness and discriminativity to the task of video footage reuse detection in news broadcasts.

INRIA-LEAR'S Video Copy Detection System

The video copyright detection system developed for the TRECVID 2008 evaluation campaign builds upon the bag-of-features image search system proposed in [3], and provides a more precise representation by adding 1) a Hamming embedding and 2) weak geometric consistency constraints.

Rapid and sensitive sequence comparison with FASTP and FASTA.

Small codes and large image databases for recognition

The goal is to develop efficient image search and scene matching techniques that are not only fast, but also require very little memory, enabling their use on standard hardware or even on handheld devices.