ML Anthology

Latent Model Clustering and Applications to Visual Recognition

Simon Polak, Amnon Shashua
ICCV 2007 pp. 1-8
doi:10.1109/ICCV.2007.4409051 /iccv/2007/polak2007iccv-latent/

Abstract

We consider clustering situations in which the pairwise affinity between data points depends on a latent "context" variable. For example, when clustering features arising from multiple object classes the affinity value between two image features depends on the object class that generated those features. We show that clustering in the context of a latent variable can be represented as a special 3D hyper- graph and introduce an algorithm for obtaining the clusters. We use the latent clustering model for an unsupervised multiple object class recognition where feature fragments are shared among multiple clusters and those in turn are shared among multiple object classes.

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Text

Polak and Shashua. "Latent Model Clustering and Applications to Visual Recognition." IEEE/CVF International Conference on Computer Vision, 2007. doi:10.1109/ICCV.2007.4409051

Markdown

[Polak and Shashua. "Latent Model Clustering and Applications to Visual Recognition." IEEE/CVF International Conference on Computer Vision, 2007.](https://mlanthology.org/iccv/2007/polak2007iccv-latent/) doi:10.1109/ICCV.2007.4409051

BibTeX

@inproceedings{polak2007iccv-latent,
  title     = {{Latent Model Clustering and Applications to Visual Recognition}},
  author    = {Polak, Simon and Shashua, Amnon},
  booktitle = {IEEE/CVF International Conference on Computer Vision},
  year      = {2007},
  pages     = {1-8},
  doi       = {10.1109/ICCV.2007.4409051},
  url       = {https://mlanthology.org/iccv/2007/polak2007iccv-latent/}
}