Salient object detection via spectral graph weighted low rank matrix recovery

Abstract A novel saliency detection method via spectral graph (SG) weighted low rank matrix recovery (LR) is presented in this paper. The location, color, and boundary priors are exploited in many LR-based saliency detection methods. However, these priors do not work well when the salient objects are far away from image center, especially when the background is complicated and has low contrast with objects. Because spectral graph contains rich image contrast, it is used as an efficient weight to obtain a much reasonable high-level prior in the proposed LR-based saliency model. Compared with previous LR-based methods, low rank matrix and sparse matrix rather than only sparse matrix are used to calculate the final saliency by an integration function and an activation function. The numerical and visual results on four challenging salient object datasets show that our method performs competitively for salient object detection task against some recent state-of-the-art algorithms.