For unsupervised image retrieval, which features are chosen for final representation determines its performance. Nowadays, unsupervised methods can deal with most image retrieval tasks. However, it is still a challenging task to retrieve images with complex background. In this paper, we propose a new approach of mask-based prominent feature accumulation (MPFA), which utilizes MAX-Mask and SUM-Mask to retain significant features in each channel for all database images. Channels are then sorted by MPFA to select representative channels of feature maps extracted from pre-trained CNN. After that, the final image representation is generated by aggregating the selected channels. Experiments on the public datasets show improvement of our proposed approach compared to state-of-the-art methods, especially for images with complex background.