Efficient Hierarchical Graph-Based Video Segmentation

We present an efficient and scalable technique for spatiotemporal segmentation of long video sequences using a hierarchical graph-based algorithm. We begin by oversegmenting a volumetric video graph into space-time regions grouped by appearance. We then construct a "region graph" over the obtained segmentation and iteratively repeat this process over multiple levels to create a tree of spatio-temporal segmentations. This hierarchical approach generates high quality segmentations, which are temporally coherent with stable region boundaries, and allows subsequent applications to choose from varying levels of granularity. We further improve segmentation quality by using dense optical flow to guide temporal connections in the initial graph. We also propose two novel approaches to improve the scalability of our technique: (a) a parallel outof-core algorithm that can process volumes much larger than an in-core algorithm, and (b) a clip-based processing algorithm that divides the v...

• Type: Conference
• Year: 2010
• Where: CVPR
• Authors: Matthias Grundmann, Vivek Kwatra, Mei Han, Irfan Essa
• Keywords: Computer Vision, CVPR 2010, Hierarchical Graph-based Algorithm, Long Videos, We Begin