This paper presents a visual saliency detection approach, which is a hybrid of local feature-based saliency and global feature-based saliency (simply called local saliency and global saliency, respectively, for short). First, we propose an automatic selection of smoothing parameter scheme to make the foreground and background of an input image more homogeneous. Then, we partition the smoothed image into a set of regions and compute the local saliency by measuring the color and texture dissimilarity in the smoothed regions and the original regions, respectively. Furthermore, we utilize the global color distribution model embedded with color coherence, together with the multiple edge saliency, to yield the global saliency. Finally, we combine the local and global saliencies, and utilize the composition information to obtain the final saliency. Experimental results show the efficacy of the proposed method, featuring: 1) the enhanced accuracy of detecting visual salient region and appearance in comparison with the existing counterparts, 2) the robustness against the noise and the low-resolution problem of images, and 3) its applicability to multisaliency detection task.