Latent Dirichlet Allocation for Spatial Analysis of Satellite Images

This paper describes research that seeks to supersede human inductive learning and reasoning in high-level scene understanding and content extraction. Searching for relevant knowledge with a semantic meaning consists mostly in visual human inspection of the data, regardless of the application. The method presented in this paper is an innovation in the field of information retrieval. It aims to discover latent semantic classes containing pairs of objects characterized by a certain spatial positioning. A hierarchical structure is recommended for the image content. This approach is based on a method initially developed for topics discovery in text, applied this time to invariant descriptors of image region or objects configurations. First, invariant spatial signatures are computed for pairs of objects, based on a measure of their interaction, as attributes for describing spatial arrangements inside the scene. Spatial visual words are then defined through a simple classification, extracting new patterns of similar object configurations. Further, the scene is modeled according to these new patterns (spatial visual words) using the latent Dirichlet allocation model into a finite mixture over an underlying set of topics. In the end, some statistics are done to achieve a better understanding of the spatial distributions inside the discovered semantic classes.