Content-based image retrieval (CBIR) is a challenging task in computer vision. Its goal is to find images among the database images which contain the same instance as the query image. A typical image retrieval approach contains two steps: extract a proper representation vector from each raw image, and then retrieve via nearest neighbor search on those representations. The quality of the image representation vector extracted from raw image is the key factor to determine the overall performance of an image retrieval approach. Image retrieval have witnessed two developing stages, namely hand-craft feature based approaches and deep feature based approaches. Furthermore, there are two phases in each stage, i.e., one phase of using global feature and another phase of using local feature based approaches. Due to the limited representation power of hand-craft features, nowadays, the research focus of image retrieval has shifted to how to make the full utility of deep features. In this study, we give a brief review of the development progress of unsupervised image retrieval based on different ways to extract image representations. Several representative unsupervised image retrieval approaches are then introduced and compared on benchmark image retrieval datasets. At last, we discuss a few future research perspectives.