Saliency detection task has witnessed a booming interest for years, due to the growth of the computer vision community. In this project propose, a new saliency model that performs active learning with kernelized subspace ranker (KSR) referred to as KSR-AL. This pool based active learning algorithm ranks the in formativeness of unlabeled data by considering both uncertainty sampling and information density, thereby minimizing the cost of labeling. The informative images are selected to train the KSR iteratively and incrementally. The learning model of this algorithm is designed on object level proposals and region-based convolutional neural network (RCNN) features, by jointly learning a Rank-SVM classifier and a subspace projection. When the active learning process meets its stopping criteria, the saliency map of each image is generated by a weight fusion of its top-ranked proposals, whose ranking scores are graded by the learned ranker. We show that the KSRAL achieves a reduction in annotation, as well as improvement in performance, compared with the supervised learning scheme. Besides, the proposed algorithm also out performs the state-of-the art methods. These improvements are demonstrated by extensive experiments on six publicly available benchmark datasets. This project is implemented with MATLAB software.