In this paper, we propose an effective superpixel-based saliency model. First, the original image is simplified by performing superpixel segmentation and adaptive color quantization. On the basis of superpixel representation, inter-superpixel similarity measures are then calculated based on difference of histograms and spatial distance between each pair of superpixels. For each superpixel, its global contrast measure and spatial sparsity measure are evaluated, and refined with the integration of intersuperpixel similarity measures to finally generate the superpixel-level saliency map. Experimental results on a dataset containing 1,000 test images with ground truths demonstrate that the proposed saliency model outperforms state-of-the-art saliency models.