Abstract: Due to the difficulty to indicate an object for the existent visual saliency models and the limitation of representation ability with single feature, an algorithm of object extraction was projected combined with multi-feature and visual saliency analysis. Firstly, for the train images, the random windows uniformly distributed over the entire image were sampled and the prior information of parameters were learned through the analysis of multi-feature. Secondly, for the test image, the random windows were sampled too and their scores were calculated integrated the analysis of characteristics above in Bayes Model. Finally, the regions output by this method were arranged according to their weights and the region most possibly contained object was determined combined with the analysis of saliency and its criterion, thus the target detection was achieved ultimately. The results showed that the algorithm has an active selectivity to the candidate regions depended on the analysis of visual saliency and has a distinguish ability between target and non-target due to the combination of multi-feature