Batch mode Adaptive Multiple Instance Learning for computer vision tasks

@article{Li2012BatchMA,
  title={Batch mode Adaptive Multiple Instance Learning for computer vision tasks},
  author={Wen Li and Lixin Duan and Ivor W. Tsang and Dong Xu},
  journal={2012 IEEE Conference on Computer Vision and Pattern Recognition},
  year={2012},
  pages={2368-2375}
}
Multiple Instance Learning (MIL) has been widely exploited in many computer vision tasks, such as image retrieval, object tracking and so on. To handle ambiguity of instance labels in positive bags, the training process of traditional MIL methods is usually computationally expensive, which limits the applications of MIL in more computer vision tasks. In this paper, we propose a novel batch mode framework, namely Batch mode Adaptive Multiple Instance Learning (BAMIL), to accelerate the instance… CONTINUE READING
Highly Cited
This paper has 25 citations. REVIEW CITATIONS

Citations

Publications citing this paper.
Showing 1-10 of 14 extracted citations

References

Publications referenced by this paper.
Showing 1-10 of 28 references

SimpleMKL

  • A. Rakotomamonjy, F. R. Bach, Y. Grandvalet
  • JMLR, 9:2491–2521,
  • 2008
Highly Influential
6 Excerpts

Similar Papers

Loading similar papers…