In this paper, we present an approach towards automated story picturing based on mutual reinforcement principle. Story picturing refers to the process of illustrating a story with suitable pictures. In our approach, semantic keywords are extracted from the story text and an annotated image database is searched to form an initial picture pool. Thereafter, a novel image ranking scheme automatically determines the importance of each image. Both lexical annotations and visual content of an image play a role in determining its rank. Annotations are processed using the Wordnet to derive a lexical signature for each image. An integrated region based similarity is also calculated between each pair of images. An overall similarity measure is formed using lexical and visual features. In the end, a mutual reinforcement based rank is calculated for each image using the image similarity matrix. We also present a human behavior model based on a discrete state Markov process which captures the intuition for our technique. Experimental results have demonstrated the effectiveness of our scheme.