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    • 1. 发明授权
    • Enriching online videos by content detection, searching, and information aggregation
    • 通过内容检测,搜索和信息聚合丰富在线视频
    • US09443147B2
    • 2016-09-13
    • US12767114
    • 2010-04-26
    • Tao MeiXian-Sheng HuaShipeng Li
    • Tao MeiXian-Sheng HuaShipeng Li
    • G06K9/00H04N21/462H04N21/4722G06F17/30G06Q30/02
    • G06K9/00751G06F17/30017G06F17/30828G06K9/00765G06Q30/02H04N21/4622H04N21/4722
    • Many internet users consume content through online videos. For example, users may view movies, television shows, music videos, and/or homemade videos. It may be advantageous to provide additional information to users consuming the online videos. Unfortunately, many current techniques may be unable to provide additional information relevant to the online videos from outside sources. Accordingly, one or more systems and/or techniques for determining a set of additional information relevant to an online video are disclosed herein. In particular, visual, textual, audio, and/or other features may be extracted from an online video (e.g., original content of the online video and/or embedded advertisements). Using the extracted features, additional information (e.g., images, advertisements, etc.) may be determined based upon matching the extracted features with content of a database. The additional information may be presented to a user consuming the online video.
    • 许多互联网用户通过在线视频消费内容。 例如,用户可以观看电影,电视节目,音乐视频和/或自制视频。 向消费在线视频的用户提供附加信息可能是有利的。 不幸的是,许多当前的技术可能无法提供与来自外部来源的在线视频相关的附加信息。 因此,本文公开了用于确定与在线视频相关的一组附加信息的一个或多个系统和/或技术。 特别地,可以从在线视频(例如,在线视频和/或嵌入式广告的原始内容)提取视觉,文本,音频和/或其他特征。 使用所提取的特征,可以基于将提取的特征与数据库的内容相匹配来确定附加信息(例如,图像,广告等)。 附加信息可以被呈现给使用在线视频的用户。
    • 5. 发明授权
    • Supervised re-ranking for visual search
    • 视觉搜索的监督重新排名
    • US08543521B2
    • 2013-09-24
    • US13076350
    • 2011-03-30
    • Linjun YangXian-Sheng Hua
    • Linjun YangXian-Sheng Hua
    • G06F15/18
    • G06F17/30274G06F17/30247G06F17/30268
    • Supervised re-ranking for visual search may include re-ordering images that are returned in response to a text-based image search by exploiting visual information included in the images. In one example, supervised re-ranking for visual search may include receiving a textual query, obtaining an initial ranking result including a plurality of images corresponding to the textual query, and representing the textual query by a visual context of the plurality of images. A query-independent re-ranking model may be trained based on visual re-ranking features of the plurality of images of the textual query in accordance with a supervised training algorithm.
    • 视觉搜索的监督重新排序可以包括通过利用包括在图像中的视觉信息来重新排序响应于基于文本的图像搜索返回的图像。 在一个示例中,用于视觉搜索的监督重新排序可以包括接收文本查询,获得包括对应于文本查询的多个图像的初始排名结果,以及通过多个图像的视觉上下文来表示文本查询。 可以根据监督训练算法,基于文本查询的多个图像的视觉重新排列特征来训练不依赖于查询的重排序模型。
    • 9. 发明授权
    • Image search result summarization with informative priors
    • 图像搜索结果汇总与信息先验
    • US08346767B2
    • 2013-01-01
    • US12764917
    • 2010-04-21
    • Linjun YangRui LiuXian-Sheng Hua
    • Linjun YangRui LiuXian-Sheng Hua
    • G06F17/30G06F7/00
    • G06F17/3028
    • An informative priors image search result summarization system and method that summarizes image search results based on the image relevance (as determined by a search engine's initial ranking) and the image quality. Embodiments of the system and method cluster the image search results, rank images within each cluster based on a computed image score, and then select a summary image for the cluster. Each cluster is analyzed and an image in the cluster having the maximum image score is included in a selected summary collection. The image score is computed using the image relevance and the image quality, as well as a cluster coherence, a density, and a diversity. The selection of images from a collection of candidate images generates an image search result summarization, which is presented to a user. The summaries are presented to the user in a ranked order based on their image scores.
    • 一种信息先验图像搜索结果汇总系统和方法,其基于图像相关性(由搜索引擎的初始排名确定)和图像质量来总结图像搜索结果。 系统和方法的实施例对图像搜索结果进行聚类,基于计算的图像分数对每个聚类内的图像进行排序,然后选择聚类的摘要图像。 分析每个群集,并且具有最大图像得分的群集中的图像被包括在所选择的摘要集合中。 使用图像相关性和图像质量以及簇相干性,密度和多样性来计算图像分数。 来自候选图像集合的图像的选择产生呈现给用户的图像搜索结果汇总。 这些摘要根据他们的图像分数以排序顺序呈现给用户。