In recent years, visual saliency detection has become a popular research topic. It can provide useful prior knowledge for high-level vision tasks, such as object detection and image classification. In this paper, a graph-based superpixel-wise similarity called “homology similarity” is proposed, which describes how likely two superpixels belong to the same object or background region. A saliency detection model is then developed based on the combination of homology distribution and improved color contrast. The homology distribution represents spatial compactness, while the color contrast characterizes color conspicuity. By combining these two saliency cues, the proposed model obtains more uniformly highlighted object-level saliency maps with fewer false positive noises. In the experiments, we evaluate our model and 14 competing models (including traditional and state-of-the-art models) on the most popular dataset MSRA-1000 and 4 other publicly available datasets. Experimental results show that, compared with these competing models, our model yields promising results.