A Graph-based Framework for Video Object Segmentation and Extraction in Feature Space

Video segmentation is the task of grouping pixels in successive video frames into perceptually coherent regions. It is a preliminary step to solve higher level problems such as automated surveillance, object tracking, video summarization, video indexing and retrieval. For consumer videos, video segmentation is a useful tool for extracting relevant and interesting content from such video sequence for further analysis or repurposing of the visual content. Given an unannotated video sequence captured by either a static or hand-held camera, our graph-based approach first effectively models the data in a high dimensional feature space, which emphasizes the correlation between similar pixels while reducing the interclass connectivity between different objects. The graph model fuses appearance, spatial, and temporal information to break a volumetric video sequence into semantic spatiotemporal key-segments. By further grouping the key-segments, a binary segmentation is able to extract a moving object of interest from a video sequence based on its unique and distinguishable regional properties. Experiment results show the robustness of our approach, which has achieved comparable or better performance when compared to several unsupervised methods.