Spectral salient object detection

Abstract

Many salient object detection methods first apply pre-segmentation on image to obtain over-segmented regions to facilitate subsequent saliency computation. However, these pre-segmentation methods often ignore the holistic issue of objects and could degrade object detection performance. This paper proposes a novel method, spectral salient object detection, that aims at maintaining objects holistically during pre-segmentation in order to provide more reliable feature extraction from a complete object region and to facilitate object-level saliency estimation. In the proposed method, a hierarchical spectral partition method based on the normalized graph cut (Ncut) is proposed for image segmentation phase in saliency detection, where a superpixel graph that captures the intrinsic color and edge information of an image is constructed and then hierarchically partitioned. In each hierarchy level, a region constituted by superpixels is evaluated by criteria based on figure-ground principles and statistical prior to obtain a regional saliency score. The coarse salient region is obtained by integrating multiple saliency maps from successive hierarchies. The final saliency map is derived by minimizing the graph-based semi-supervised learning energy function on the synthetic coarse saliency map. Despite the simple intuition of maintaining object holism, experimental results on 5 benchmark datasets including ASD, ECSSD, MSRA, PASCAL-S, DUT-OMRON demonstrate encouraging performance of the proposed method, along with the comparisons to 13 state-of-the-art methods. The proposed method is shown to be effective on emphasizing large/medium-sized salient objects uniformly due to the employment of Ncut. Besides, we conduct thorough analysis and evaluation on parameters and individual modules.