Salient object detection using normalized cut and geodesics

Recently the Normalized cut (Ncut) has been introduced to salient object detection [1, 2]. In this paper we validate that instead of proposing new detection models that leverage the Ncut, the previous geodesic saliency detection model which computes shortest paths on a graph can be adapted to eigenvectors of the Ncut to produce superior performance. Since the Ncut partitions a graph in a normalized energy minimization fashion, resulting eigenvectors contain decent cluster information that can group visual contents. Combining it with the existing geodesic saliency detection is conducive to highlighting salient objects uniformly, yielding to improved detection accuracy. Experiments by comparing with 12 existing methods on four benchmark datasets show the proposed method significantly outperforms the original geodesic saliency model and achieves comparable performance to state-of-the-art methods.