Spatially Regularized Latent Topic Model for Simultaneous Object Discovery and Segmentation

Latent Dirichlet Allocation (LDA) has been increasingly applied in the area of computer vision. LDA is based on the ´bag of words´ assumption that ignores the spatial structure of images. This problem poses a non-trivial impact on the performance of the model. There exist a number of methods that attempt to address the limit. One representative work can be Spatial Latent Topic Model (Spatial-LTM) for unsupervised joint object discovery and segmentation, which improves over LDA by assigning locally co-occurring visual words with the same topic. However, this model still ignores the spatial relations between visual words which are spatially distant from each other. In this paper, we add a spatial regularization term to the model´s posterior distribution that regulates the difference of multinomial weight between each pair of visual words in a topic based on their spatial distance apart in an image set. We call the improved model Spatially Regularized Latent Topic Model (SR-LTM). Experiment result shows that SR-LTM outperforms Spatial-LTM in both unsupervised object discovery accuracy and segmentation accuracy.