The Social Picture: Advanced Image Analysis Applications

Abstract

In The Social Picture (TSP) an huge amount of crowdsourced social images can be collected and explored. We distinguish three main kind of events: public, private and cultural heritage related ones. The framework embeds a number of advanced Computer Vision algorithms, able to capture the visual content of images and organize them in a semantic way. In this paper we employ VisualSFM (VSFM) to add new features in TSP through the computation of a 3D sparse reconstruction of a collection within TSP. VisualSFM creates a N-View Match (NVM) file as output. Starting from this NVM file, which characterizes the 3D sparse reconstruction, we are able to build two important relationships: the one between cameras and points and the one between cameras themselves. Using these relationships, we implemented two advanced Image Analysis applications. In the first one, we consider the cameras as nodes in a fully connected graph in which the edges weights are equal to the number of matches between cameras. The spanning tree of this graph is used to explore images in a meaningful way, obtaining a scene summarization. In the second application, we define three kinds of density maps with relation to image features: density map, weighted-density map and social-weighted-density map. Results of a test conducted on a collection from TSP is shown.