In this paper

we present a general framework to discover image categories automatically.The algorithm includes two parts:1)we pose the problem of category discovery as an automated graph partition task. Each graph vertex indicates an image

and a partitioned sub-graph consisting of connected graph vertices representing a clustered category. The model of each image category can be learned by stepwise feature selection using the Adaboost algorithm. 2)A MCMC-based stochastic algorithm

the Swendsen-Wang Cuts (SWC)

is adopted to solve the graph partition fast. Compared to traditional random cluster sampling techniques

SWC converges faster. We apply our method on two image datasets

and the experimental results demonstrate superior performance of our method over other popular state-of-the-arts methods

including Kmeans

pLSA

and Affinity Propagation.