Existing saliency detection methods can not suppress the background effectively and detect the salient object accurately in complex background

a method of superpixel content-aware priors based multi-scale Bayesian saliency detection is proposed. Firstly

the image containing object is segmented into multi-scale superpixel maps

then the content-aware priors of contrast priors

center position priors

and boundary connected background priors are introduced on each scale to calculate the salient object values on a single scale; Secondly

the content-aware priors values of the various scales generate a rough saliency map; Thirdly

the rough saliency map value is used as the prior probability

and the likelihood is calculated according to the color histogram and the convex hull center

using the multi-scale Bayesian model to obtain the final salient object; Finally

three public data sets

five evaluation indicators

and seven existing methods are used for comparative experiments. The experiments show that the method has better performance in the detection of salient objects.