Photos are important carriers for destination image communication. Currently, effectively and efficiently selecting appropriate photos for destination promotion remains a major challenge for DMOs, and has caused the discrepancy between projected and received images. In the process of photo selection, contents that can best evoke viewers’ potential motives should be highly considered. This project proposes and implements a machine learning based model to assist DMOs with photo content selection. This protocol can rank candidate photos describing a specific theme from viewers’ perspective. In our study, over 190,000 Flickr photos of New York City were analyzed to demonstrate the effectiveness of our approach. The results indicate that the proposed method can facilitate the selection of destination photos and address the well-known gap between the projected image and the received image.