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To fully dig the image data information, a retrieval method based on directed graph model is proposed, for improving performance of image retrieval by combining first retrieve with distance metric and second retrieve with directed graph distance. First, it executes first retrieve by using traditional features including texture, edge and color and the Euclidean distance among features, and obtains a query sort list; On this basis, it builds directed graph models with different correlation metric thresholds, according to correlation metric that is designed by combining with distance metric and cosine metric; finally, it computes directed graph distance and executes second retrieve, for reducing false retrieval phenomena. The results of image retrieval experiments on COREL and Image CLEF datasets show that, this method has average precision and average recall.