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    • 3. 发明申请
    • Robust Large-Scale Visual Codebook Construction
    • 坚固的大型视觉代码簿构建
    • US20120251007A1
    • 2012-10-04
    • US13077735
    • 2011-03-31
    • Linjun YangDarui LiXian-Sheng HuaHong-Jiang Zhang
    • Linjun YangDarui LiXian-Sheng HuaHong-Jiang Zhang
    • G06K9/46
    • G06K9/6223
    • Techniques for construction of a visual codebook are described herein. Feature points may be extracted from large numbers of images. In one example, images providing N feature points may be used to construct a codebook of K words. The centers of each of K clusters of feature points may be initialized. In a looping or iterative manner, an assignment step assigns each feature point to a cluster and an update step locates a center of each cluster. The feature points may be assigned to a cluster based on a lesser of a distance to a center of a previously assigned cluster and a distance to a center derived by operation of an approximate nearest neighbor algorithm having aspects of randomization. The loop terminates when the feature points have sufficiently converged to their respective clusters. Centers of the clusters represent visual words, which may be used to construct the visual codebook.
    • 本文描述了构建视觉码本的技术。 特征点可以从大量图像中提取出来。 在一个示例中,提供N个特征点的图像可以用于构造K个字的码本。 可以初始化K个特征点中的每一个的中心。 以循环或迭代的方式,分配步骤将每个特征点分配给集群,并且更新步骤定位每个集群的中心。 可以基于距先前分配的簇的中心的距离中较小的一个特征点来分配特征点,以及通过具有随机化方面的近似最近邻算法的操作导出的到中心的距离。 当特征点已经充分收敛到它们各自的簇时,环路终止。 集群的中心表示视觉词,可用于构建视觉码本。
    • 4. 发明授权
    • Robust large-scale visual codebook construction
    • 坚固的大型视觉代码簿建设
    • US08422802B2
    • 2013-04-16
    • US13077735
    • 2011-03-31
    • Linjun YangDarui LiXian-Sheng HuaHong-Jiang Zhang
    • Linjun YangDarui LiXian-Sheng HuaHong-Jiang Zhang
    • G06K9/36
    • G06K9/6223
    • Techniques for construction of a visual codebook are described herein. Feature points may be extracted from large numbers of images. In one example, images providing N feature points may be used to construct a codebook of K words. The centers of each of K clusters of feature points may be initialized. In a looping or iterative manner, an assignment step assigns each feature point to a cluster and an update step locates a center of each cluster. The feature points may be assigned to a cluster based on a lesser of a distance to a center of a previously assigned cluster and a distance to a center derived by operation of an approximate nearest neighbor algorithm having aspects of randomization. The loop terminates when the feature points have sufficiently converged to their respective clusters. Centers of the clusters represent visual words, which may be used to construct the visual codebook.
    • 本文描述了构建视觉码本的技术。 特征点可以从大量图像中提取出来。 在一个示例中,提供N个特征点的图像可以用于构造K个字的码本。 可以初始化K个特征点中的每一个的中心。 以循环或迭代的方式,分配步骤将每个特征点分配给集群,并且更新步骤定位每个集群的中心。 可以基于距先前分配的簇的中心的距离中较小的一个特征点来分配特征点,以及通过具有随机化方面的近似最近邻算法的操作导出的到中心的距离。 当特征点已经充分收敛到它们各自的簇时,环路终止。 集群的中心表示视觉词,可用于构建视觉码本。
    • 5. 发明申请
    • COLORBLIND ACCESSIBLE IMAGE SEARCH
    • COLORBLIND可访问的图像搜索
    • US20100185624A1
    • 2010-07-22
    • US12355795
    • 2009-01-18
    • Meng WangLinjun YangXian-Sheng Hua
    • Meng WangLinjun YangXian-Sheng Hua
    • G06F17/30
    • G06F17/3025
    • Colorblind accessible image search technique embodiments are presented that re-rank the results of a relevance-ranked image search to account for the accessibility of the images to a colorblind person. This is accomplished by first computing a colorblind accessibility quantity for each image of interest in the search results. A colorblind accessibility quantity quantizes the degree to which color information is preserved when an image is perceived by a colorblind person viewing the image. It is computed by generating a colorblind version of an image that simulates how the image would appear to the colorblind person. An amount quantifying the loss of color information between the image and the colorblind version of the image is then estimated. This estimate is used to compute the colorblind accessibility quantity for the image. Once the colorblind accessibility quantities have been computed, the image search results are re-ranked based on these quantities.
    • 呈现了彩色盲可访问图像搜索技术实施例,其对排序相关的图像搜索的结果进行重新排序,以便将图像的可访问性应用于彩色盲人。 这是通过首先计算搜索结果中感兴趣的每个图像的色盲辅助数量来实现的。 彩色可见量可以量化当查看图像的彩色盲人感知到图像时保留颜色信息的程度。 它通过生成图像的彩色版本来计算,该图像模拟图像将如何显示给彩色盲人。 然后估计量化图像和图像的彩色版本之间的颜色信息的损失的量。 该估计用于计算图像的色盲辅助数量。 一旦计算了色盲辅助数量,就会根据这些数量重新排列图像搜索结果。
    • 6. 发明授权
    • Visual and textual query suggestion
    • 视觉和文本查询建议
    • US08452794B2
    • 2013-05-28
    • US12369421
    • 2009-02-11
    • Linjun YangMeng WangZhengjun ZhaTao MeiXian-Sheng Hua
    • Linjun YangMeng WangZhengjun ZhaTao MeiXian-Sheng Hua
    • G06F7/00G06F17/30
    • G06F17/3064G06F17/30277G06F17/30864
    • Techniques described herein enable better understanding of the intent of a user that submits a particular search query. These techniques receive a search request for images associated with a particular query. In response, the techniques determine images that are associated with the query, as well as other keywords that are associated with these images. The techniques then cluster, for each set of images associated with one of these keywords, the set of images into multiple groups. The techniques then rank the images and determine a representative image of each cluster. Finally, the tools suggest, to the user that submitted the query, to refine the search based on user selection of a keyword and a representative image. Thus, the techniques better understand the user's intent by allowing the user to refine the search based on another keyword and based on an image on which the user wishes to focus the search.
    • 本文描述的技术能够更好地理解提交特定搜索查询的用户的意图。 这些技术接收与特定查询相关联的图像的搜索请求。 作为响应,这些技术确定与查询相关联的图像以及与这些图像相关联的其他关键词。 然后,对于与这些关键词之一相关联的每组图像,该技术将该组图像聚类成多个组。 然后,技术对图像进行排序并确定每个聚类的代表图像。 最后,工具向提交查询的用户建议,根据用户对关键字和代表图像的选择来优化搜索。 因此,这些技术通过允许用户基于另一个关键字来改进搜索并且基于用户希望集中搜索的图像来更好地理解用户的意图。
    • 8. 发明授权
    • Colorblind accessible image search
    • Colorblind可访问图像搜索
    • US08412694B2
    • 2013-04-02
    • US12355795
    • 2009-01-18
    • Meng WangLinjun YangXian-Sheng Hua
    • Meng WangLinjun YangXian-Sheng Hua
    • G06F7/00G06F17/30
    • G06F17/3025
    • Colorblind accessible image search technique embodiments are presented that re-rank the results of a relevance-ranked image search to account for the accessibility of the images to a colorblind person. This is accomplished by first computing a colorblind accessibility quantity for each image of interest in the search results. A colorblind accessibility quantity quantizes the degree to which color information is preserved when an image is perceived by a colorblind person viewing the image. It is computed by generating a colorblind version of an image that simulates how the image would appear to the colorblind person. An amount quantifying the loss of color information between the image and the colorblind version of the image is then estimated. This estimate is used to compute the colorblind accessibility quantity for the image. Once the colorblind accessibility quantities have been computed, the image search results are re-ranked based on these quantities.
    • 呈现了彩色盲可访问图像搜索技术实施例,其对排序相关的图像搜索的结果进行重新排序,以便将图像的可访问性应用于彩色盲人。 这是通过首先计算搜索结果中感兴趣的每个图像的色盲辅助数量来实现的。 彩色可见量可以量化当查看图像的彩色盲人感知到图像时保留颜色信息的程度。 它通过生成图像的彩色版本来计算,该图像模拟图像将如何显示给彩色盲人。 然后估计量化图像和图像的彩色版本之间的颜色信息的损失的量。 该估计用于计算图像的色盲辅助数量。 一旦计算了色盲辅助数量,就会根据这些数量重新排列图像搜索结果。
    • 10. 发明申请
    • Visual and Textual Query Suggestion
    • 视觉和文本查询建议
    • US20100205202A1
    • 2010-08-12
    • US12369421
    • 2009-02-11
    • Linjun YangMeng WangZhengjun ZhaTao MeiXian-Sheng Hua
    • Linjun YangMeng WangZhengjun ZhaTao MeiXian-Sheng Hua
    • G06F17/30
    • G06F17/3064G06F17/30277G06F17/30864
    • Techniques described herein enable better understanding of the intent of a user that submits a particular search query. These techniques receive a search request for images associated with a particular query. In response, the techniques determine images that are associated with the query, as well as other keywords that are associated with these images. The techniques then cluster, for each set of images associated with one of these keywords, the set of images into multiple groups. The techniques then rank the images and determine a representative image of each cluster. Finally, the tools suggest, to the user that submitted the query, to refine the search based on user selection of a keyword and a representative image. Thus, the techniques better understand the user's intent by allowing the user to refine the search based on another keyword and based on an image on which the user wishes to focus the search.
    • 本文描述的技术能够更好地理解提交特定搜索查询的用户的意图。 这些技术接收与特定查询相关联的图像的搜索请求。 作为响应,这些技术确定与查询相关联的图像以及与这些图像相关联的其他关键词。 然后,对于与这些关键词之一相关联的每组图像,该技术将该组图像聚类成多个组。 然后,技术对图像进行排序并确定每个聚类的代表图像。 最后,工具向提交查询的用户建议,根据用户对关键字和代表图像的选择来优化搜索。 因此,这些技术通过允许用户基于另一个关键字来改进搜索并且基于用户希望集中搜索的图像来更好地理解用户的意图。