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OPTIMAL IMAGE TRANSFORMATION BASED ON PROFESSIONALISM SCORE OF SUBJECT

机译:基于专业分数的最优图像变换

摘要

In an example embodiment, an image transformation is automatically performed on a digital image to improve perceived professionalism of a subject of the image. A machine learning algorithm is utilized to generate a professionalism score for the digital image, the utilizing a machine learning algorithm comprising: a training mode where a plurality of sample images with labeled professionalism scores are used to train a classification function in a model that produces as professionalism score as output; an analysis mode where the model is used to generate a professionalism score for the digital image. Then the professionalism score is used as an input to a continuous variable optimization algorithm to determine an optimum version of the digital image from a plurality of possible versions of the digital image on which one or more image transformations have been performed, using the classification function.
机译:在示例实施例中,在数字图像上自动执行图像变换以改善图像对象的感知专业性。机器学习算法用于为数字图像生成专业评分,该机器学习算法包括:训练模式,其中多个带有标记专业得分的样本图像用于训练模型函数中的分类函数,该模型产生专业水平得分作为输出;一种分析模式,其中该模型用于为数字图像生成专业评分。然后,将专业分数用作连续变量优化算法的输入,以使用分类函数从数字图像的多个可能版本中确定数字图像的最佳版本,在该多个数字版本中已经对其进行了一个或多个图像变换。

著录项

  • 公开/公告号US2017301077A1

    专利类型

  • 公开/公告日2017-10-19

    原文格式PDF

  • 申请/专利权人 LINKEDLN CORPORATION;

    申请/专利号US201615098950

  • 发明设计人 URI MERHAV;DAN SHACHAM;

    申请日2016-04-14

  • 分类号G06T7;G06T5;G06K9/66;G06T3/60;

  • 国家 US

  • 入库时间 2022-08-21 13:52:15

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