Object segmentation from consumer videos: a unified framework based on visual attention

Abstract

The purpose of video object segmentation is to automatically extract objects of interest from consumer videos. This paper investigates this problem from a novel perspective of human visual attention. We roughly classify visual attentions in video scenes into two categories: static attention and dynamic attention. The static attention model is mainly responsible for segmenting the interesting objects without motion, whereas the dynamic attention model plays an important role in obtaining the interesting objects with motion. The fusion of both models allows us to obtain all interesting objects using a unified framework. This framework is easy to implement and has the great promise to become a basic tool for many content-based consumer video applications. Experimental results demonstrate the good performance of our algorithm.