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公开(公告)号：US09177207B2
公开(公告)日：2015-11-03
申请号：US14599454
申请日：2015-01-16
Applicant: Yahoo! Inc.
Inventor： Lyndon Kennedy , Roelof van Zwol , Nicolas Torzec , Belle Tseng
CPC classification number: G06K9/00711 , G06K9/00228 , G06K9/46 , G06K9/4671 , G06K9/52 , G06K9/62 , G06K9/6201 , G06K2009/4666 , G06T3/40 , G06T11/60
Abstract: Software for supervised learning extracts a set of pixel-level features from each source image in collection of source images. Each of the source images is associated with a thumbnail created by an editor. The software also generates a collection of unique bounding boxes for each source image. And the software calculates a set of region-level features for each bounding box. Each region-level feature results from the aggregation of pixel values for one of the pixel-level features. The software learns a regression model, using the calculated region-level features and the thumbnail associated with the source image. Then the software chooses a thumbnail from a collection of unique bounding boxes in a new image, based on application of the regression model. The software uses a thumbnail received from an editor instead of the chosen thumbnail, if the chosen thumbnail is of insufficient quality as measured against a scoring threshold.
Abstract translation: 用于监督学习的软件在收集源图像时从每个源图像中提取一组像素级特征。 每个源图像与由编辑器创建的缩略图相关联。 该软件还为每个源图像生成一组独特的边界框。 并且软件为每个边界框计算一组区域级别的功能。 每个区域级别的特征来自于像素级特征之一的像素值的聚合。 该软件学习回归模型，使用计算的区域级功能和与源图像相关联的缩略图。 然后，软件根据回归模型的应用，从新图像中的独特边界框的集合中选择一个缩略图。 如果所选缩略图的质量不足，则根据评分阈值测量，软件将使用从编辑器接收的缩略图而不是所选的缩略图。
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公开(公告)号：US20150131900A1
公开(公告)日：2015-05-14
申请号：US14599454
申请日：2015-01-16
Applicant: Yahoo! Inc.
Inventor： Lyndon Kennedy , Roelof van Zwol , Nicolas Torzec , Belle Tseng
CPC classification number: G06K9/00711 , G06K9/00228 , G06K9/46 , G06K9/4671 , G06K9/52 , G06K9/62 , G06K9/6201 , G06K2009/4666 , G06T3/40 , G06T11/60
Abstract: Software for supervised learning extracts a set of pixel-level features from each source image in collection of source images. Each of the source images is associated with a thumbnail created by an editor. The software also generates a collection of unique bounding boxes for each source image. And the software calculates a set of region-level features for each bounding box. Each region-level feature results from the aggregation of pixel values for one of the pixel-level features. The software learns a regression model, using the calculated region-level features and the thumbnail associated with the source image. Then the software chooses a thumbnail from a collection of unique bounding boxes in a new image, based on application of the regression model. The software uses a thumbnail received from an editor instead of the chosen thumbnail, if the chosen thumbnail is of insufficient quality as measured against a scoring threshold.
Abstract translation: 用于监督学习的软件在收集源图像时从每个源图像中提取一组像素级特征。 每个源图像与由编辑器创建的缩略图相关联。 该软件还为每个源图像生成一组独特的边界框。 并且软件为每个边界框计算一组区域级别的功能。 每个区域级别的特征来自于像素级特征之一的像素值的聚合。 该软件学习回归模型，使用计算的区域级功能和与源图像相关联的缩略图。 然后，软件根据回归模型的应用，从新图像中的独特边界框的集合中选择一个缩略图。 如果所选缩略图的质量不足，则根据评分阈值测量，软件将使用从编辑器接收的缩略图而不是所选的缩略图。
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