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    • 1. 发明申请
    • VISUAL CLOTHING RETRIEVAL
    • 视觉衣物检索
    • US20140314313A1
    • 2014-10-23
    • US13865142
    • 2013-04-17
    • YAHOO! INC.
    • Lyndon KennedyJia LiIoannis Kalantidis
    • G06K9/62G06T7/00
    • G06T7/0079G06K9/00369G06T7/10G06T7/70G06T2207/10024G06T2207/20021G06T2207/20076G06T2207/20081G06T2207/30196
    • Techniques are provided for efficiently identifying relevant product images based on product items detected in a query image. In general, a query image may represent a digital image in any format that depicts a human body and one or more product items. For example, a query image may be an image for display on a webpage, an image captured by a user using a camera device, or an image that is part of a media content item, such as a frame from a video. Product items may be detected in a query image by segmenting the query image into a plurality of image segments and clustering one or more of the plurality image segments into one or more image segment clusters. The resulting image segments and image segment clusters may be used to search for visually similar product images.
    • 提供了基于在查询图像中检测到的产品项目来有效地识别相关产品图像的技术。 通常,查询图像可以表示描绘人体和一个或多个产品项目的任何格式的数字图像。 例如,查询图像可以是用于在网页上显示的图像,由使用相机设备的用户捕获的图像,或作为诸如来自视频的帧的媒体内容项的一部分的图像。 可以通过将查询图像分割成多个图像片段并将多个图像片段中的一个或多个聚类成一个或多个图像片段群集来在查询图像中检测产品项目。 所得到的图像段和图像段聚类可用于搜索视觉上相似的产品图像。
    • 3. 发明授权
    • Visual clothing retrieval
    • 视觉服装检索
    • US09460518B2
    • 2016-10-04
    • US13865142
    • 2013-04-17
    • Yahoo! Inc.
    • Lyndon KennedyJia LiIoannis Kalantidis
    • G06T7/00G06K9/00
    • G06T7/0079G06K9/00369G06T7/10G06T7/70G06T2207/10024G06T2207/20021G06T2207/20076G06T2207/20081G06T2207/30196
    • Techniques are provided for efficiently identifying relevant product images based on product items detected in a query image. In general, a query image may represent a digital image in any format that depicts a human body and one or more product items. For example, a query image may be an image for display on a webpage, an image captured by a user using a camera device, or an image that is part of a media content item, such as a frame from a video. Product items may be detected in a query image by segmenting the query image into a plurality of image segments and clustering one or more of the plurality image segments into one or more image segment clusters. The resulting image segments and image segment clusters may be used to search for visually similar product images.
    • 提供了基于在查询图像中检测到的产品项目来有效地识别相关产品图像的技术。 通常,查询图像可以表示描绘人体和一个或多个产品项目的任何格式的数字图像。 例如,查询图像可以是用于在网页上显示的图像,由使用相机设备的用户捕获的图像,或作为诸如来自视频的帧的媒体内容项的一部分的图像。 可以通过将查询图像分割成多个图像片段并将多个图像片段中的一个或多个聚类成一个或多个图像片段群集来在查询图像中检测产品项目。 所得到的图像段和图像段聚类可用于搜索视觉上相似的产品图像。
    • 8. 发明授权
    • Image cropping using supervised learning
    • 使用监督学习的图像裁剪
    • US09177207B2
    • 2015-11-03
    • US14599454
    • 2015-01-16
    • Yahoo! Inc.
    • Lyndon KennedyRoelof van ZwolNicolas TorzecBelle Tseng
    • G06K9/00G06T3/40G06T11/60G06K9/46G06K9/52G06K9/62
    • G06K9/00711G06K9/00228G06K9/46G06K9/4671G06K9/52G06K9/62G06K9/6201G06K2009/4666G06T3/40G06T11/60
    • 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.
    • 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。