  This paper investigates a general framework to discover categories of
unlabeled scene images according to their appearances (i.e., textures and
structures). We jointly solve the two coupled tasks in an unsupervised manner:
(i) classifying images without pre-determining the number of categories, and
(ii) pursuing generative model for each category. In our method, each image is
represented by two types of image descriptors that are effective to capture
image appearances from different aspects. By treating each image as a graph
vertex, we build up an graph, and pose the image categorization as a graph
partition process. Specifically, a partitioned sub-graph can be regarded as a
category of scenes, and we define the probabilistic model of graph partition by
accumulating the generative models of all separated categories. For efficient
inference with the graph, we employ a stochastic cluster sampling algorithm,
which is designed based on the Metropolis-Hasting mechanism. During the
iterations of inference, the model of each category is analytically updated by
a generative learning algorithm. In the experiments, our approach is validated
on several challenging databases, and it outperforms other popular
state-of-the-art methods. The implementation details and empirical analysis are
presented as well.
