An improved fusion algorithm based on Bayesian formula is proposed for addressing inaccurate detection and unclear edge problems in existing image saliency detection algorithms. First, compactness prior is used for obtaining the primary saliency map. Then, the secondary saliency map is obtained via multi-kernel learning using primary saliency maps as training samples. Finally, a Bayesian formula is used to integrate the primary saliency map with the secondary saliency map at a certain proportion to obtain an accurate saliency map. Experimental results obtained on two public datasets demonstrate that the proposed algorithm can effectively highlight the target object and remove blurred edges. The proposed algorithm is superior to eight existing algorithms from the viewpoint of accuracy, recall rate, and F-measure value. Furthermore, the running speed of the proposed algorithm is faster, and it demonstrates more accurate experimental results.