I'm interested in learning a topic model from a bag of visual words for image retrieval. I can compute V cluster centers (visual words) of SIFT descriptors at keypoints for each training image and fit, for example, an LDA topic model for a collection of training images. This will produce a set of topics and topic proportions for each training image. Now, given a new test image, I want to figure out what the topic proportions of that image are using already learned topics so I can retrieve the closest image from the training set. Is there a simple way of doing that?