Mural images have large intra-class variance and strong background noise. We present a clustered multiple instance learning strategy to classify murals of different periods and styles. We divide the sample space into several different sub-spaces

and a classification model is trained for each sub-space with training samples falling into this sub-space. In the testing stage

we choose a classification model for the testing sample according to the sub-space it falls into. In each classifier's training

we treat each mural image sample as a "bag" which contains a set of instances

and we use multiple instance learning to train the classifier. In the training process

we introduce hidden variables to identify each instance

the presence of hidden variables makes the classifier's optimization problem not convex which cannot be directly solved using a gradient descent. In this paper we use an iterative process to train Latent SVM(support vector machine) as the classifier for each sub-space. The experimental results indicate that our classification model can improve the classification accuracy of mural images by about 5% with comparison to the baseline method. The strategy proposed in this paper can greatly reduce the impact of the intra-class variance and background noise brings to the classification result of mural images.