With the rapid development of hardware and related technologies, salient object detection based on deep learning methods has become one of the popular research topics in computer vision applications. For the detection focused on the integrity of salient objects, edge accuracy of objects is one of the important indicators in the evaluation of visual saliency detection. However, in deep learning-based methods, complex networks and large amounts of data are usually required to achieve good boundary accuracy. To solve this issue, a scale-reductive pooling approach with clustering-based majority-take-all strategy is proposed in this paper. According to the experimental results, we show that the prediction results are improved with reasonable quantity of superpixels.