Saliency detection

which is based on the simulation of human visual attention

is an important way to help computer sensors to understand the world. Saliency detection has many applications in computer vision

such as image segmentation

image retrieval

and retargeting. However

saliency detection is a challenging computer vision task. Most of the existing saliency algorithms can only detect pixels or regions of interest. A new method based on hierarchical information fusion is proposed in this study to distinguish the saliency object from the complex background region and guarantee the uniformity of patches in the same object. The proposed method is different from the state-of-art method that uses mid-level superpixels and object-level regions to adjust the raw saliency map. First

an edge-preserving filtering is adopted as a pretreatment and then the mid-level superpixels are generated by the simple linear iterative clustering algorithm. Second

the mid-level raw saliency map is obtained by the saliency filter and adjusted by two priors

which can reduce the influence of complex background regions. Afterward

the mid-level superpixels are clustered to object-level segments by spectral clustering

and an object boundary prior is defined to enhance the consistency of the saliency map. Finally

the saliency label will be diffused from superpixels to object-level regions by heat diffusion. The evaluation experiments against 16 other methods are conducted on the benchmark MSRA1000 database by the precision-recall curve and the F-measure score. By utilizing the mid-level superpixels and object-level clustering regions

our method can reflect the hierarchical relationship between patches and objects well. The experimental result show that our method is also applicable to multi-target saliency detection.