Fast retargeting with adaptive grid optimization

Bing Li, Yiming Chen, Jinqiao Wang, Ling-Yu Duan, Wen Gao

Abstract: Effective and efficient retargeting techniques may enrich users' browsing experiences in mobile devices. Existing mesh-based retargeting solutions put less efforts in making well-tuned meshes. In this paper, we propose a novel adaptive grid based optimization method to retarget an image. First, we present an entropy based measure to guide the grid construction. Then we employ the quadtree structure to adjust the grid granularity adaptively. Furthermore, to reduce the inappropriate deformation from inconsistent importance assignment, we build a global optimization model to alleviate serious shape deformation in retargeting. Comparison experiments show our method's superiority over the state-of-the-art approaches.