Dim small target detection problem in infrared complex background was formulated as a binary classification problem of background and target in the theoretical framework of Markov random field (MRF). Based on the posterior probability model of MRF, a method using prior information of target SCR (signal-to-clutter ratio) and local statistic characteristic of infrared image was proposed to construct the posterior probability model of observed image. The classic iterated conditional mode (ICM) was used to estimate the optimal labeling image. Simulation and experimental results show that the proposed algorithm effectively reduces the false labeling probability of background, while maintaining a high probability of correct labeling of target. In addition, for using image’s local statistic characteristic in modeling, the proposed algorithm also reduces the correlation between labeled results and model parameters which contributes to improvement on the convergence speed of estimating the optimal labeling.