Discovering and recommending points of interest (POI) are drawing more attention to meet the increasing demand from personalized tours. Unlike conventional systems focusing on popular sightseeing locations, we develop a system, Anaba, to discover the obscure sightseeing spots that are less well-known while still worth visiting. By analyzing geo-tagged images on image hosting websites (Flickr, etc.), Anaba discovers and ranks sightseeing spots based on their obscurity levels and scenery quality. Anaba first selects obscure candidates in accordance with the asymmetry between visitors who are familiar with a target area and those who are not. Then, it evaluates the scenery quality of each candidate by considering both social appreciation and the content of images shot around there. The experiments on a newly retrieved dataset demonstrate the effectiveness of the proposed system.