A unified approach to salient object detection via low rank matrix recovery. CVPR

Salient object detection is not a pure low-level, bottom-up process. Higher-level knowledge is important even for task-independent image saliency. We propose a unified model to incorporate traditional low-level features with higher-level guidance to detect salient objects. In our mod-el, an image is represented as a low-rank matrix plus sparse noises in a certain feature space, where the non-salient re-gions (or background) can be explained by the low-rank matrix, and the salient regions are indicated by the sparse noises. To ensure the validity of this model, a linear trans-form for the feature space is introduced and needs to be learned. Given an image, its low-level saliency is then ex-tracted by identifying those sparse noises when recovering the low-rank matrix. Furthermore, higher-level knowledge is fused to compose a prior map, and is treated as a prior term in the objective function to improve the performance. Extensive experiments show that our model can comfortably achieves comparable performance to the existing methods even without the help from high-level knowledge. The in-tegration of top-down priors further improves the perfor-mance and achieves the state-of-the-art. Moreover, the pro-posed model can be considered as a prototype framework not only for general salient object detection, but also for potential task-dependent saliency applications. 1