Spectral salient object detection
Journal article, 2018

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.

Salient object detection

Normalized cut

Graph partition

Object holism

Regional saliency


Keren Fu

Chalmers, Electrical Engineering, Signal Processing and Biomedical Engineering

Irene Yu-Hua Gu

Chalmers, Electrical Engineering, Signal Processing and Biomedical Engineering

Jie Yang


0925-2312 (ISSN)

Vol. 275 788-803

Areas of Advance


Subject Categories

Signal Processing

Computer Vision and Robotics (Autonomous Systems)



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