LAUSR

Salient object detection model with active environment perception can substantially facilitate a wide range of applications. Conventional models primarily rely on handcrafted low-level image features or high-level features. However, these models may face great challenges in low-lighting scenario, due to the lack of well-defined features to represent saliency information in low-contrast images. In this paper, we propose a novel deep neural network framework embedded with covariance descriptor for salient object detection in low-contrast images. Several low-level features are extracted to compute their mutual covariance, which is then trained via a 7-layer convolutional neural network (CNN). The saliency map can be generated by estimating the saliency score of each region via the pre-trained CNN model. Extensive experiments have been conducted on six challenging datasets to evaluate the performance of the proposed model against ten state-of-the-art models.