Scene Recognition by Jointly Modeling Latent Topics

Abstract

We present a new topic model, named supervised Mixed Membership Stochastic Block Model, to recognize scene categories. In contrast to previous topic model based scene recognition, its key advantage originates from the joint modeling of the latent topics of adjacent visual words to promote the visual coherency of the latent topics. To ensure that an image is only a sparse mixture of latent topics, we use a Gini impurity based regularizer to control the freedom of a visual word taking different latent topics. We further show that the proposed model can be easily extended to incorporate the global spatial layout of the latent topics. Combined together, latent topic coherency and sparsity can rule out unlikely combinations of latent topics and guide classifier to produce more semantically meaningful interpretation of the scene. The model parameters are learned using Gibbs sampling algorithm, and the model is evaluated on three datasets, i.e. Scene-15, LabelMe, and UIUC-Sports. Experimental results demonstrate the superiority of our method over other related methods.