Visual summarization of landmarks via viewpoint modeling

In this paper, we describe an approach for visually summarizing a landmark by recommending images with diverse viewpoints (e.g. front-side viewpoint, bottom-top viewpoint, close-distant viewpoint, etc). Our approach models an image's viewpoint using a 4-D viewpoint vector, which describes viewpoint in horizontal, vertical, scale and orientation aspects. To construct the viewpoint vector for an image, we select Identical Semantic Points (ISPs) from hundreds to thousands SIFT points of the image to captures some major and unique parts of a landmark. Then a four dimensional viewpoint vector is utilized to measure on the position coordinate, scale and orientation of the ISPs in an image. After that, we perform viewpoint clustering to finally summarize landmarks. We evaluate our approach on 5K Oxford building image set and provide final summarization results for some famous landmarks in Oxford.