Unsupervised salient object detection aims to automatically detect important or attractive target objects in the scene without any user annotation. Compared to fully supervised, it can save a lot of manpower and resources invested in pixel-level annotation. However, the performance of existing unsupervised methods is still far from satisfactory because of the interference of complex backgrounds and the lack of knowledge in the semantic layer of scene objects. To solve these challenging problems, in this paper, we propose a new bottom-up method to better highlight the integral salient object region by exploiting the co-occurrence of saliency and objectness properties. Specifically, saliency estimation effectively utilizes low-level visual features based on high-quality hierarchical image segmentation (convolutional oriented boundaries). The objectness scoring is mainly obtained through the improved object proposals mapping map. Experiment results demonstrate that the proposed method can efficiently remove the interference of non-salient objects and complex background while preserving more salient object regions.