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Aesthetic Image Enhancement by Dependence-Aware Object Recomposition

Published: 01 November 2013 Publication History

Abstract

This paper proposes an image-enhancement method to optimize photograph composition by rearranging foreground objects in the photograph. To adjust objects' positions while keeping the original scene content, we first perform a novel structure dependence analysis on the image to obtain the dependencies between all background regions. To determine the optimal positions for foreground objects, we formulate an optimization problem based on widely used heuristics for aesthetically pleasing pictures. Semantic relations between foreground objects are also taken into account during optimization. The final output is produced by moving foreground objects, together with their dependent regions, to optimal positions. The results show that our approach can effectively optimize photographs with single or multiple foreground objects without compromising the original photograph content.

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  • (2024)A novel approach using deep convolutional neural network to classify the photographs based on leading-line by fine-tuning the pre-trained VGG16 neural networkMultimedia Tools and Applications10.1007/s11042-022-13338-583:1(3189-3214)Online publication date: 1-Jan-2024
  • (2023)Rule-of-Thirds or Centered? A study in preference in photo compositionSIGGRAPH Asia 2023 Posters10.1145/3610542.3626121(1-2)Online publication date: 12-Dec-2023
  • (2023)Salient-Centeredness and Saliency Size in Computational AestheticsACM Transactions on Applied Perception10.1145/358831720:2(1-23)Online publication date: 21-Apr-2023
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  1. Aesthetic Image Enhancement by Dependence-Aware Object Recomposition

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    cover image IEEE Transactions on Multimedia
    IEEE Transactions on Multimedia  Volume 15, Issue 7
    November 2013
    243 pages

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    IEEE Press

    Publication History

    Published: 01 November 2013

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    Cited By

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    • (2024)A novel approach using deep convolutional neural network to classify the photographs based on leading-line by fine-tuning the pre-trained VGG16 neural networkMultimedia Tools and Applications10.1007/s11042-022-13338-583:1(3189-3214)Online publication date: 1-Jan-2024
    • (2023)Rule-of-Thirds or Centered? A study in preference in photo compositionSIGGRAPH Asia 2023 Posters10.1145/3610542.3626121(1-2)Online publication date: 12-Dec-2023
    • (2023)Salient-Centeredness and Saliency Size in Computational AestheticsACM Transactions on Applied Perception10.1145/358831720:2(1-23)Online publication date: 21-Apr-2023
    • (2023)Selective video enhancement in the Laguerre–Gauss domainImage Communication10.1016/j.image.2022.116876110:COnline publication date: 1-Jan-2023
    • (2021)Aesthetic-guided outward image croppingACM Transactions on Graphics10.1145/3478513.348056640:6(1-13)Online publication date: 10-Dec-2021
    • (2020)Adaptive Photographic Composition GuidanceProceedings of the 2020 CHI Conference on Human Factors in Computing Systems10.1145/3313831.3376635(1-13)Online publication date: 21-Apr-2020
    • (2020)Computational Approaches to Aesthetic Quality Assessment of Digital Photographs: State of the Art and Future Research DirectivesPattern Recognition and Image Analysis10.1134/S105466182004008230:4(593-606)Online publication date: 1-Oct-2020
    • (2019)Color Theme--based Aesthetic Enhancement Algorithm to Emulate the Human Perception of Beauty in PhotosACM Transactions on Multimedia Computing, Communications, and Applications10.1145/332899115:2s(1-17)Online publication date: 3-Jul-2019
    • (2019)Harvesting Visual Objects from Internet Images via Deep-Learning-Based Objectness AssessmentACM Transactions on Multimedia Computing, Communications, and Applications10.1145/331846315:3(1-23)Online publication date: 8-Aug-2019
    • (2019)Improving Saliency Detection Based on Modeling Photographer's IntentionIEEE Transactions on Multimedia10.1109/TMM.2018.285138921:1(124-134)Online publication date: 1-Jan-2019
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