Visual Saliency Detection Using Spatiotemporal Decomposition

We propose a novel technique for detection of visual saliency in dynamic video based on video decomposition. The decomposition obtains the sparse features in a particular orientation by exploiting the spatiotemporal discontinuities present in a video cube. A weighted sum of the sparse features along three orthogonal directions determines the salient regions in the video cubes. The weights computed using the frame correlation along three directions are based on the characteristic of human visual system that identifies the sparsest feature as the most salient feature in a video. Unlike the existing methods, which detect the salient region as blob, the proposed approach detects the exact boundaries of salient region with minimum false detection. The experimental results confirm that the detected salient regions of a video closely resemble the salient regions detected by actual tracking of human eyes. The algorithm is tested on different types of video contents and compared with the several state-of-the-art methods to establish the effectiveness of the proposed method.