In this paper, we address invariant scene classification from images. We propose a novel descriptor based on the statistical characterization of the spatial patterns formed by elementary objects in images. Elementary objects are defined from a tree of shapes of the topology map of the image and each object is characterized by shape context feature vector. Viewing the set of elementary objects as a realization of a random spatial process, we investigate a statistical analysis using log-Gaussian Cox model to define an invariant image descriptor. An application to natural scene recognition is described. Reported results validate the proposed descriptor with respect to previous work.