日本語
 
User Manual Privacy Policy ポリシー/免責事項 連絡先
  詳細検索ブラウズ

アイテム詳細


公開

会議論文

Preserving Modes and Messages via Diverse Particle Selection

MPS-Authors
/persons/resource/persons85114

Zuffi,  Sylvia
Dept. Perceiving Systems, Max Planck Institute for Intelligent Systems, Max Planck Society;

/persons/resource/persons75293

Black,  Michael J.
Dept. Perceiving Systems, Max Planck Institute for Intelligent Systems, Max Planck Society;

URL
There are no locators available
フルテキスト (公開)
公開されているフルテキストはありません
付随資料 (公開)
There is no public supplementary material available
引用

Pacheco, J., Zuffi, S., Black, M. J., & Sudderth, E. (2014). Preserving Modes and Messages via Diverse Particle Selection. In E. P., Xing, & T., Jebara (Eds.), Proceedings of the 31st International Conference on Machine Learning (ICML 2014) (pp. 1152-1160). Brookline, MA: Microtome Publishing. Retrieved from http://jmlr.csail.mit.edu/proceedings/papers/v32/pacheco14.pdf.


引用: http://hdl.handle.net/11858/00-001M-0000-0024-DD2B-6
要旨
In applications of graphical models arising in domains such as computer vision and signal processing, we often seek the most likely configurations of high-dimensional, continuous variables. We develop a particle-based max-product algorithm which maintains a diverse set of posterior mode hypotheses, and is robust to initialization. At each iteration, the set of hypotheses at each node is augmented via stochastic proposals, and then reduced via an efficient selection algorithm. The integer program underlying our optimization-based particle selection minimizes errors in subsequent max-product message updates. This objective automatically encourages diversity in the maintained hypotheses, without requiring tuning of application-specific distances among hypotheses. By avoiding the stochastic resampling steps underlying particle sum-product algorithms, we also avoid common degeneracies where particles collapse onto a single hypothesis. Our approach significantly outperforms previous particle-based algorithms in experiments focusing on the estimation of human pose from single images.