Geometric LDA: A Generative Model for Particular Object Discovery
James Philbin, Josef Sivic and Andrew Zisserman
In: Proceedings of the British Machine Vision Conference(2008).

Abstract

Automatically organizing collections of images presents serious challenges to the current state-of-the art methods in image data mining. Often, what is required is that images taken in the same place, of the same thing, or of the same person be conceptually grouped together. To achieve this, we introduce the Geometric Latent Dirichlet Allocation (gLDA) model for unsupervised particular object discovery in unordered image collections. This explicitly represents documents as mixtures of particular objects or facades, and builds rich latent topic models which incorporate the identity and locations of visual words specific to the topic in a geometrically consistent way. Applying standard inference techniques to this model enables images likely to contain the same object to be probabilistically grouped and ranked. We demonstrate the model on a publicly available dataset of Oxford images, and show examples of spatially consistent groupings.

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