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 grid-based 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, digital matting and photo browsing. Experimental results show comparable performance compared to the-state-of-art on common data sets.