Spatial Latent Dirichlet Allocation

By Xiaogang Wang, W. Grimson. Neural Information Processing Systems, 2007

306 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/596f30a4fd9ac3d4fc01f7c4e705f8821f471f84

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Citations in the corpus

Topic modelling for qualitative studies (2017, background): “Relational topic models construct a hierarchical model that reflects the structure of a document network as a graph [17], while Spatial LDA extends the LDA approach to image recognition by imposing spatial structure on ‘visual words’ [7].”
Semantic Annotation of Satellite Images Using Latent Dirichlet Allocation (2010, methodology): “Different from [14], where the spatial information is modeled, in this letter, the spatial information is introduced without any explicit model.”
A Bayesian Model for Simultaneous Image Clustering, Annotation and Object Segmentation (2009, methodology): “A relatively complete summary of the l it rature may be found in [16, 23], where the advantages of the approaches in [16, 23] are descri b d relative to previous related approaches [3, 6, 10, 11, 18, 24, 27].”
Dependent Multinomial Models Made Easy: Stick-Breaking with the Polya-gamma Augmentation (2015, methodology): “for images [10]), easy, effective, modular inference for CTMs and DTMs is a promising general tool.”
Unsupervised Activity Perception in Crowded and Complicated Scenes Using Hierarchical Bayesian Models (2008, methodology): “In [43], we proposed a model allowing random assignment of words to documents according to some prior which encodes temporal information.”
Towards total scene understanding: Classification, annotation and segmentation in an automatic framework (2009, methodology): “The object recognition and segmentation part of our model is related to a few recent unsupervised object categorization and segmentation papers [4, 27, 32].”
Tile-Level Annotation of Satellite Images Using Multi-Level Max-Margin Discriminative Random Field (2013, methodology): “Various extensions of the aspect models have been also designed, e.g. the spatial LDA (SLDA) model [7].”
Unsupervised Object Discovery: A Comparison (2010, background): “Recently, unsupervised object discovery methods including spatial information (Wang and Grimson 2008; Todorovic and Ahuja 2006) and hierarchical organization of categories (Sivic et al. 2008; Bart et al. 2008) have been proposed as well, but these fall outside the scope of this paper.”
Visual Information in Semantic Representation (2010, background): “Image descriptors found closely to each other are likely to represent the same object and should form one term rather than several distinct ones (Wang and Grimson, 2007).”