Evaluating the performance of image fusion algorithms objectively is still an open problem. We proposed an integrational approach to objective image fusion evaluation based on integrating different existing objective metrics in order to overcome their individual shortcomings. The process of the objective measure construction contains two steps: first, we construct a candidate measure set, where each element contains high evaluation accuracy and low correlation with each other; then, the weights of measures in constructing the target measure are determined dynamically on training image set. Considering that the true quality of fused images is a subjective perception, we introduce assessment indefiniteness when calculating the evaluation accuracy of each measure to reduce the influence of errors of subjective perception. Experiments on various images are conducted to test the effectiveness of the proposed measure. The results have shown that the measure is useful in conjunction with the other objective measures to account for qualitative subjective perception.