Context saliency based image summarization
Image summarization is to determine a smaller but faithful representation of the original visual content. In this paper, we propose a context saliency based image summarization approach, incorporating statistical saliency and geometric information as the importance measurement instead of visual saliency. To ensure image summaries to be adaptive to target device under perception constraint, we present a gridbased piecewise linear image warping scaleplate, and adopt the sweet spot evaluation to generate a flexible model combining the cropping and warping methods. Additionally, we explore potential extensions on image retargeting, thumbnail generation, digitalmatting and photo browsing. Experimental results show comparable performance compared to the-stateofart on common data sets.