The traditional saliency detection method is difficult to detect different kinds of saliency target simultaneously.In order to solve this problem

an algorithm based on unsupervised SDAE network is proposed in this paper.The stacked denoising auto-encoder (SDAE) network is used to sparsely reconstruct original image in multiple scales.The difference between the original image and the reconstructed image is used as a saliency map

and the binaryzation of the saliency map is used as salient detection result.In the process of SDAE network training

the original image is used as the original data and the reconstructed images are treated as observed data.In order to improve the efficiency of network training

the deep belief network (DBN) is trained by greedy method in each layer without supervising

and the network parameters are delivered to stacked denoising auto-encoder (SDAE) network as initial parameters.Then

the mutual information between the original data and the observed data is used as loss function

and the network parameters are tuned by backpropagation.The experiments show that the proposed algorithm can accomplish the saliency detection of various targets

which has the advantages of good universality and high accuracy.