Image aesthetics enhancement using composition-based saliency detection
Visual saliency detection and segmentation are widely used in many applications in image processing and computer vision. However, existing saliency detection methods have not fully taken the spatial information of salient regions into account. Inspired by the basic photographic composition rules, we present a novel saliency detection method, which utilizes the knowledge of photographic composition as priors to improve the saliency detection results. Moreover, an online parameter selection method is proposed when utilizing GrabCut to achieve the saliency segmentation result. Besides, to test the applicability of our method, we present a novel post-processing framework for the photographs to be more artistic. The salient region and depth map are firstly computed. The salient region keeps its sharpness, while other parts in the photograph get blurred based on the depth map. To our best knowledge, this is a novel image-based attempt to enhance aesthetics by post-processing a photograph via realistic blurring. We test our method on the 1,000 benchmark test images and dataset MSRA. Extensive experimental results show the applicability and effectiveness of our method.
saliency detection saliency segmentation realistic blurring photography composition depth of field