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    • 41. 发明申请
    • DIGITAL IMAGE RETRIEVAL BY AGGREGATING SEARCH RESULTS BASED ON VISUAL ANNOTATIONS
    • 通过视觉观察聚合搜索结果的数字图像检索
    • US20100114888A1
    • 2010-05-06
    • US12258349
    • 2008-10-24
    • Roelof van ZwolXimena Olivares
    • Roelof van ZwolXimena Olivares
    • G06F17/30
    • G06F17/30265
    • An approach for responding to a text-based query for a digital image is provided. A request that identifies one or more keywords is received. A number of annotated digital images are selected. Each selected annotated digital image has a bounded region, on its appearance, that has an annotation associated with at least one of the keywords. A set of candidate digital images is selected for each annotated digital image. The set of candidate images, for a particular annotated digital image, are the digital images, of a set of digital images, which have an appearance that is most similar to the particular annotated digital image. The sets of candidate images are aggregated into a single set of digital images. A response is generated that identifies those digital images in the single set of digital images which are most responsive to the one or more keywords.
    • 提供了一种用于响应数字图像的基于文本的查询的方法。 接收到识别一个或多个关键字的请求。 选择了许多带注释的数字图像。 每个选择的注释数字图像具有在其外观上具有与至少一个关键字相关联的注释的有界区域。 为每个带注释的数字图像选择一组候选数字图像。 用于特定注释数字图像的候选图像集合是一组数字图像的数字图像,其具有与特定注释数字图像最相似的外观。 候选图像的集合被聚合成单个数字图像集合。 产生响应,其识别对一个或多个关键词最响应的单个数字图像集合中的那些数字图像。
    • 47. 发明申请
    • RANKING AND SELECTING REPRESENTATIVE VIDEO IMAGES
    • 排名和选择代表性视频图像
    • US20130142418A1
    • 2013-06-06
    • US13312558
    • 2011-12-06
    • Roelof van ZwolLluis Garcia Pueyo
    • Roelof van ZwolLluis Garcia Pueyo
    • G06K9/62
    • G06K9/00751
    • Techniques are described herein for selecting representative images for video items using a trained machine learning engine. A training set is fed to a machine learning engine. The training set includes, for each image in the training set, input parameter values and an externally-generated score. Once a machine learning model has been generated based on the training set, input parameters for unscored images are fed to the trained machine learning engine. Based on the machine learning model, the trained machine learning engine generates scores for the images. To select a representative image for a particular video item, candidate images for that particular video item may be ranked based on their scores, and the candidate image with the top score may be selected as the representative image for the video item.
    • 本文描述了使用训练有素的机器学习引擎来选择用于视频项目的代表性图像的技术。 训练组被馈送到机器学习引擎。 对于培训集中的每个图像,训练集包括输入参数值和外部生成的分数。 一旦基于训练集产生了机器学习模型,则将未分级图像的输入参数馈送到经过训练的机器学习引擎。 基于机器学习模型,训练有素的机器学习引擎为图像生成分数。 为了选择特定视频项目的代表性图像,可以基于它们的分数对该特定视频项目的候选图像进行排名,并且可以选择具有最高分数的候选图像作为视频项目的代表图像。