Abstract：The existing saliency detection methods could only detect the significant part of the target object but cannot completely detect the saliency objects.In this work，a complete detection method of saliency objects is proposed.Firstly，hierarchical segmentation approach is utilized to achieve the boundary of all objects and endow them with the initial saliency value in terms of the area and connectivity of each object and obtain grayscale image.Then，in order to achieve the super saliency image and superpixels，adaptive threshold segmentation approach is chose to deal with the cluster-based salient image.Finally，according to the scale of superpixels of each object in the grayscale image，we achieve corresponding object postprocessing image.The final complete saliency image is obtained by combining complete processing image with super saliency image in a linear summation.The proposed method is illustrated on two public datasets and compares with other state-of-the-art methods.Extensive experiments demonstrate that either saliency detection in single image or cosaliency detection in multiple images，the proposed schemes to be superior terms of both precision and recall.
2016, Vol. 37 
