Saliency detection is a fundamental issue in computer vision. It is widely applied in fixation prediction

object detection

scene classification

and other visual tasks. In order to improve the precision of visual saliency detection with multi-features

a multi-feature integration algorithm is proposed based on the joint probability distribution of saliency map and combined with priori knowledge. Firstly

the potential defects of single feature saliency detection are analyzed

and the joint probability distribution of saliency maps with multiple features is deduced. Secondly

the priori distribution of the saliency map is deduced based on the rarity

sparsity

compactness and center priori of the saliency map

and the condition distribution of the saliency map is simplified based on the assumption of normal distribution. Then the maximum a posteriori estimation is obtained from the joint probability distribution of the saliency map

and a supervised learning model of the distribution parameters is constructed based on the multi-threshold hypothesis. Experiments show that compared to the highest-precision saliency detection method on single feature

the mean average error of the multi-feature algorithm under the supervised and heuristic method is decreased by 6.98% and 6.81%

and the average F-measure is improved by 1.19% and 1.16%. And the multi-feature integration of single image takes only 11.8ms. The algorithm has high accuracy and real-time performance

and can be combined with the required features and different prior information according to the task. It meets the requirements of saliency detection with multi-features.