Unsupervised Learning of Categorical Segments in Image Collections

Marco Andreetto, Lihi Zelnik-Manor, Pietro Perona

CVPRW 2008 pp. 1-8

doi:10.1109/CVPRW.2008.4562972 /cvprw/2008/andreetto2008cvprw-unsupervised/

Abstract

Which one comes first: segmentation or recognition? We propose a unified framework for carrying out the two simultaneously and without supervision. The framework combines a flexible probabilistic model, for representing the shape and appearance of each segment, with the popular “bag of visual words” model for recognition. If applied to a collection of images, our framework can simultaneously discover the segments of each image and the correspondence between such segments, without supervision. Such recurring segments may be thought of as the “parts” of corresponding objects that appear multiple times in the image collection. Thus, the model may be used for learning new categories, detecting/classifying objects, and segmenting images, without using expensive human annotation.

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Cite

Text

Andreetto et al. "Unsupervised Learning of Categorical Segments in Image Collections." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2008. doi:10.1109/CVPRW.2008.4562972

Markdown

[Andreetto et al. "Unsupervised Learning of Categorical Segments in Image Collections." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2008.](https://mlanthology.org/cvprw/2008/andreetto2008cvprw-unsupervised/) doi:10.1109/CVPRW.2008.4562972

BibTeX

@inproceedings{andreetto2008cvprw-unsupervised,
  title     = {{Unsupervised Learning of Categorical Segments in Image Collections}},
  author    = {Andreetto, Marco and Zelnik-Manor, Lihi and Perona, Pietro},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
  year      = {2008},
  pages     = {1-8},
  doi       = {10.1109/CVPRW.2008.4562972},
  url       = {https://mlanthology.org/cvprw/2008/andreetto2008cvprw-unsupervised/}
}