Spatial topic pyramid model: topic model with regional spatial information

Abstract

Latent Dirichlet allocation is the prevalent topic model and performs well for image classification. However, it ignores visual word spatial information, which affects topic assignment accuracy. This paper proposes an effective topic model framework based on spatial pyramids including visual word regional information: spatial topic pyramid model (STPM). STPM divides the images into different scale regions and uses the regional topic distributions to represent the images. The regional topic distributions effectively represent image characteristics, because they include global information (regarding the image as a single region) and the regional relationships of visual words in different scale regions. Since the pyramid layers are independent, different topic models and parameters can be used for different scale layers. It makes STPM flexible and easily extensible.