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Image aesthetics enhancement using composition-based saliency detection

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Abstract

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.

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Acknowledgment

This work is partly supported by National Program on Key Basic Research Project (973 Program) under Grant 2013CB329301, National High-tech R&D Program of China (2013AA01A601), 100 Talents Program of The Chinese Academy of Sciences, the NSFC (under Grant 61202166), and Doctoral Fund of Ministry of Education of China (under Grant 20120032120042).

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Authors and Affiliations

  1. School of Computer Science and Technology, Tianjin University, Tianjin, 300072, China

    Handong Zhao, Jingjing Chen & Yahong Han

  2. State Key Laboratory of Information Security, Institute of Information Engineering, Chinese Academy of Sciences, Beijing, 100093, China

    Xiaochun Cao

  3. Tianjin Key Laboratory of Cognitive Computing and Application, Tianjin University, Tianjin, China

    Yahong Han

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  1. Handong Zhao
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  2. Jingjing Chen
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  4. Xiaochun Cao
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Correspondence to Yahong Han.

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Zhao, H., Chen, J., Han, Y. et al. Image aesthetics enhancement using composition-based saliency detection. Multimedia Systems 21, 159–168 (2015). https://doi.org/10.1007/s00530-014-0373-1

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