Recent works have demonstrated that the convolutional descriptor aggregation can provide state-of-the-art performance for image retrieval. In this paper, we propose a multi-center convolutional descriptor aggregation (MCDA) method to produce global image representation for image retrieval. We first present a feature map center selection method to eliminate the background information in the feature maps. We then propose the channel weighting and spatial weighting schemes based on the centers to boost the effect of the features on the object. Finally, the weighted convolutional descriptors are aggregated to represent images. Experiments demonstrate that MCDA can produce state-of-the-art retrieval performance, and the generated activation map is also effective for object localization.