Automatic image cropping based on bottom-up saliency and top-down semantics

Automatic image cropping techniques have been developed recently to address the mismatch between the native display and image characteristics, such as resolution, aspect ratio, etc. These techniques usually rely on determining the importance of various regions in the image, or the aesthetic appeal of the final cropped image. In this work, we present a cropping method that combines bottom-up visual saliency and top-down semantic analysis to create a cropped image that best preserves important image content. Experimental results illustrate that the new method outperforms popular saliency-based cropping, which only relies on bottom-up analysis.