A unified framework for spatiotemporal salient region detection

Abstract

This article presents a new bottom-up framework for spatiotemporal salient region detection. The generated saliency map can uniformly highlight the salient regions. In the proposed framework, the spatial visual saliency and the temporal visual saliency are first computed, respectively, then they are fused with a dynamic scheme to generate the final spatiotemporal saliency map. In the spatial attention model, the approach of joint embedding of spatial and color cues is adopted to compute the spatial saliency map. In the temporal attention model, we propose a novel histogram of average optical flow to measure the motion contrast of the different pixels. The method can suppress the motion noise efficiently because the statistical distribution of optical flow in a patch is comparatively stable. Furthermore, we combine the spatial and the temporal saliency maps through an adaptive fusion method, in which a novel motion entropy is proposed to evaluate the motion contrast of the input video. Extensive experiments demonstrate that our method can obtain higher quality saliency map compared with state-of-the-art methods. © 2013 Wu et al.; licensee Springer.