Ryo Yonetani, Kris Kitani, Yoichi Sato: “Visual Motif Discovery via First-Person Vision”, European Conference on Computer Vision (ECCV2016), Amsterdam, Netherlands, Oct 2016 [PDF]
Visual motifs are images of visual experiences that are significant and shared across many people, such as an image of an informative sign viewed by many people and that of a familiar social situation such as when interacting with a clerk at a store. The goal of this study is to discover visual motifs from a collection of first-person videos recorded by a wearable camera. To achieve this goal, we develop a commonality clustering method that leverages three important aspects: inter-video similarity, intra-video sparseness, and people’s visual attention. The problem is posed as normalized spectral clustering, and is solved efficiently using a weighted covariance matrix. Experimental results suggest the effectiveness of our method over several state-of-the-art methods in terms of both accuracy and efficiency of visual motif discovery.