With the explosion of the number of images in personal and on-line collections, efficient techniques for navigating, indexing, labeling and searching images become more and more important. In this work we will rely on the image content as the main source of information to retrieve images. We study the representation of images by topic models in its various aspects and extend the current models. Starting from a bag-of-visual-words image description based on local image features, images representations are learned in an unsupervised fashion and each image is modeled as a mixture of topics/object parts depicted in the image. Thus topic models allow us to automatically extract high-level image content descriptions which in turn can be used to find similar images. Further, the typically lowdimensional topic-model-based representation enables efficient and fast search, especially in very large databases.