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    • 24. 发明申请
    • Automatic Video Recommendation
    • 自动视频推荐
    • US20090006368A1
    • 2009-01-01
    • US11771219
    • 2007-06-29
    • Tao MeiXian-Sheng HuaBo YangLinjun YangShipeng Li
    • Tao MeiXian-Sheng HuaBo YangLinjun YangShipeng Li
    • G06F17/30G06F3/00
    • H04N7/17318G06F16/735G06F16/78G06F16/7844G06F16/7847H04N21/466H04N21/4667H04N21/472
    • Automatic video recommendation is described. The recommendation does not require an existing user profile. The source videos are directly compared to a user selected video to determine relevance, which is then used as a basis for video recommendation. The comparison is performed with respect to a weighted feature set including at least one content-based feature, such as a visual feature, an aural feature and a content-derived textural feature. Multimodal implementation including multimodal features (e.g., visual, aural and textural) extracted from the videos is used for more reliable relevance ranking. One embodiment uses an indirect textural feature generated by automatic text categorization based on a set of predefined category hierarchy. Another embodiment uses self-learning based on user click-through history to improve relevance ranking.
    • 描述了自动视频推荐。 该建议不需要现有的用户配置文件。 源视频直接与用户选择的视频进行比较,以确定相关性,然后将其用作视频推荐的基础。 相对于包括至少一个基于内容的特征(例如视觉特征,听觉特征和内容导出的纹理特征)的加权特征集执行比较。 使用从视频提取的包括多模态特征(例如,视觉,听觉和纹理)的多模实现用于更可靠的相关性排名。 一个实施例使用基于一组预定义类别层次的自动文本分类生成的间接纹理特征。 另一个实施例使用基于用户点击历史的自学习来提高相关性排名。
    • 26. 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。
    • 27. 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。
    • 29. 发明申请
    • MEDIA TAG RECOMMENDATION TECHNOLOGIES
    • 媒体标签推荐技术
    • US20120265772A1
    • 2012-10-18
    • US13537802
    • 2012-06-29
    • Linjun YangLei WuXian-Sheng Hua
    • Linjun YangLei WuXian-Sheng Hua
    • G06F17/30
    • G06F17/3089G06Q10/10
    • Technologies for recommending relevant tags for the tagging of media based on one or more initial tags provided for the media and based on a large quantity of other tagged media. Sample media as candidates for recommendation are provided by a set of weak rankers based on corresponding relevance measures in semantic and visual domains. The various samples provided by the weak rankers are then ranked based on relative order to provide a list of recommended tags for the media. The weak rankers provide sample tags based on relevance measures including tag co-occurrence, tag content correlation, and image-conditioned tag correlation.
    • 基于为媒体提供的一个或多个初始标签并基于大量其他标记的媒体来推荐用于标记媒体的相关标签的技术。 作为推荐候选人的示例媒体由一组基于语义和视觉领域中的相应相关性度量的弱排名者提供。 然后由弱排名者提供的各种样本根据相关顺序排列,以提供媒体推荐标签的列表。 弱排名者基于相关性测量提供样本标签,包括标签共现,标签内容相关性和图像条件标签相关性。