The saliency detection methods have been widely used in the field of image processing and computer vision. However, the saliency detection algorithms via global feature and local feature extraction have shortcomings. Therefore, a significant saliency detection algorithm is proposed based on fusion of global and local features. Firstly, an image is partitioned to non-overlapped blocks. When each image block is mapped to high dimensional space by principle component analysis(PCA) method, according to the law that the isolated feature points correspond to the salient regions, the saliency map based on the global features is obtained; Secondly, based on the color dissimilarities between center block and its neighborhoods, the saliency map via the local features is obtained; Lastly, based on the Bayes theory, the two obtained saliency maps are fused to the final saliency map. The simulation results on three public image database verify that the proposed algorithm can combine the significant advantages of the global and the local saliency detection algorithms, and it is more effective on saliency detection and object segmentation compared with other state of art algorithms.