Geometric Latent Dirichlet Allocation on a Matching Graph for Large-scale Image Datasets

@article{Philbin2010GeometricLD,
  title={Geometric Latent Dirichlet Allocation on a Matching Graph for Large-scale Image Datasets},
  author={James Philbin and Josef Sivic and Andrew Zisserman},
  journal={International Journal of Computer Vision},
  year={2010},
  volume={95},
  pages={138-153}
}
Given a large-scale collection of images our aim is to efficiently associate images which contain the same entity, for example a building or object, and to discover the significant entities. To achieve this, we introduce the Geometric Latent Dirichlet Allocation (gLDA) model for unsupervised discovery of particular objects in unordered image collections. This explicitly represents images as mixtures of particular objects or facades, and builds rich latent topic models which incorporate the… CONTINUE READING
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