Following the trend of “segmentation for recognition”, we present 2LDA, a novel generative model to automatically segment an image in 2 segments, background and foreground, while inferring a latent Dirichlet allocation (LDA) topic distribution on both segments. The idea is to merge two separate modules, LDA and the segmentation module, explicitly considering (and exchanging) the uncertainty between them. The resulting model adds spatial relationships to LDA, which in turn helps in using the topics to segment an image. The experimental results show that, unlike LDA, our model can be used to recognize objects, and also outperforms the state of the art algorithms.
2lda: segmentation for recognition
PERINA, Alessandro;CRISTANI, Marco;MURINO, Vittorio
2010-01-01
Abstract
Following the trend of “segmentation for recognition”, we present 2LDA, a novel generative model to automatically segment an image in 2 segments, background and foreground, while inferring a latent Dirichlet allocation (LDA) topic distribution on both segments. The idea is to merge two separate modules, LDA and the segmentation module, explicitly considering (and exchanging) the uncertainty between them. The resulting model adds spatial relationships to LDA, which in turn helps in using the topics to segment an image. The experimental results show that, unlike LDA, our model can be used to recognize objects, and also outperforms the state of the art algorithms.
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