Image retargeting aims to automatically adjust the resolution of an image by non-homogeneously sca-ling image content and displaying it in a limited screen with well-preserved salient objects.A novel image retargeting method based on salient object detection is proposed to solve the partial distortion problem of salient objects. The proposed method utilizes the results of salient object segmentation instead of saliency maps to improve retargeting performance. First

a saliency map is generated using saliency fusion and propagation strategy

which can obtain the balance between the accuracy and completeness of salient regions. Then

based on the input image and saliency map

a saliency cut method with adaptive triple thresholding is adapted to segment salient objects

which can generate the salient objects with accurate boundaries. After this step

the curve-edge grid representation of the input image is constructed by finding the eight connected seams with the maximum energy. Finally

the grids are non-uniformly scaled to satisfy the required size. Using manual evaluation

the performance of the proposed method are compared with those of ten typical methods on RetargetMe dataset

a public dataset for image retargeting. Experimental results show that the proposed method can effectively reduce the partial distortion of salient objects in image retargeting and obtain the retargeting results without obvious artifacts on 48.8% images

a rate that is 5% better than the best existing image retargeting method. The proposed image retargeting methods based on salient object segmentation can improve the consistency of salient object processing

reduce the obvious artifacts caused by partial distortion of salient objects

and obtain good performance in retargeting.