会员体验
专利管家(专利管理)
工作空间(专利管理)
风险监控(情报监控)
数据分析(专利分析)
侵权分析(诉讼无效)
联系我们
交流群
官方交流:
QQ群: 891211   
微信请扫码    >>>
现在联系顾问~
热词
    • 3. 发明申请
    • System and method for identifying query-relevant keywords in documents with latent semantic analysis
    • 在具有潜在语义分析的文档中识别查询相关关键词的系统和方法
    • US20060106767A1
    • 2006-05-18
    • US10987377
    • 2004-11-12
    • John AdcockMatthew CooperAndreas GirgensohnLynn Wilcox
    • John AdcockMatthew CooperAndreas GirgensohnLynn Wilcox
    • G06F17/30
    • G06F17/2795G06F17/2785G06F17/30613Y10S707/99933Y10S707/99936Y10S707/99937
    • A system and method for identifying query-related keywords in documents found in a search using latent semantic analysis. The documents are represented as a document term matrix M containing one or more document term-weight vectors d, which may be term-frequency (tf) vectors or term-frequency inverse-document-frequency (tf-idf) vectors. This matrix is subjected to a truncated singular value decomposition. The resulting transform matrix U can be used to project a query term-weight vector q into the reduced N-dimensional space, followed by its expansion back into the full vector space using the inverse of U. To perform a search, the similarity of qexpanded is measured relative to each candidate document vector in this space. Exemplary similarity functions are dot product and cosine similarity. Keywords are selected with the highest values in qexpanded that are also comprised in at least one document. Matching keywords from the query may be highlighted in the search results.
    • 用于使用潜在语义分析在搜索中发现的文档中识别查询相关关键字的系统和方法。 这些文件被表示为包含一个或多个文档术语权重向量d的文档术语矩阵,其可以是术语频率(tf)向量或术语频率逆文档频率(tf) -idf)载体。 该矩阵经历截断的奇异值分解。 所得到的变换矩阵 U可用于将查询项权重向量q投影到缩小的N维空间中,然后使用 U。 为了执行搜索,相对于该空间中的每个候选文档向量测量q expanded 的相似度。 示例性相似度函数是点积和余弦相似度。 关键字被选择,其中也包含在至少一个文档中的q 扩展的最高值。 查询中的匹配关键字可能会在搜索结果中突出显示。
    • 4. 发明授权
    • System and method for identifying query-relevant keywords in documents with latent semantic analysis
    • 在具有潜在语义分析的文档中识别查询相关关键词的系统和方法
    • US07440947B2
    • 2008-10-21
    • US10987377
    • 2004-11-12
    • John E. AdcockMatthew CooperAndreas GirgensohnLynn D. Wilcox
    • John E. AdcockMatthew CooperAndreas GirgensohnLynn D. Wilcox
    • G06F7/00
    • G06F17/2795G06F17/2785G06F17/30613Y10S707/99933Y10S707/99936Y10S707/99937
    • A system and method for identifying query-related keywords in documents found in a search using latent semantic analysis. The documents are represented as a document term matrix M containing one or more document term-weight vectors d, which may be term-frequency (tf) vectors or term-frequency inverse-document-frequency (tf-idf) vectors. This matrix is subjected to a truncated singular value decomposition. The resulting transform matrix U can be used to project a query term-weight vector q into the reduced N-dimensional space, followed by its expansion back into the full vector space using the inverse of U.To perform a search, the similarity of qexpanded is measured relative to each candidate document vector in this space. Exemplary similarity functions are dot product and cosine similarity. Keywords are selected with the highest values in qexpanded that are also comprised in at least one document. Matching keywords from the query may be highlighted in the search results.
    • 用于使用潜在语义分析在搜索中发现的文档中识别查询相关关键字的系统和方法。 这些文件被表示为包含一个或多个文档术语权重向量d的文档术语矩阵,其可以是术语频率(tf)向量或术语频率逆文档频率(tf) -idf)载体。 该矩阵经受截断的奇异值分解。 所得到的变换矩阵 U可用于将查询项权重向量q投影到缩小的N维空间中,然后使用 U。 为了执行搜索,相对于该空间中的每个候选文档向量测量q expanded 的相似度。 示例性相似度函数是点积和余弦相似度。 关键字被选择,其中也包含在至少一个文档中的q 扩展的最高值。 查询中的匹配关键字可能会在搜索结果中突出显示。
    • 9. 发明申请
    • Methods and systems for discriminative keyframe selection
    • 用于辨别关键帧选择的方法和系统
    • US20050074168A1
    • 2005-04-07
    • US10678935
    • 2003-10-03
    • Matthew CooperJonathan Foote
    • Matthew CooperJonathan Foote
    • H04N5/76G06F17/30G06K9/66G06T7/00G11B27/28
    • G11B27/28G06K9/00711
    • Embodiments of the present invention provide a system and method for discriminatively selecting keyframes that are representative of segments of a source digital media and at the same time distinguishable from other keyframes representing other segments of the digital media. The method and system, in one embodiment, includes pre-processing the source digital media to obtain feature vectors for frames of the media. Discriminatively selecting a keyframe as a representative for each segment of a source digital media wherein said discriminative selection includes determining a similarity measure for each candidate keyframe and determining a dis-similarity measure for each candidate keyframe and selecting the keyframe with the highest goodness value computing from the similarity and dis-similarity measures.
    • 本发明的实施例提供了一种系统和方法,用于区分性地选择代表源数字媒体的片段的关键帧,并且同时与代表数字媒体的其他片段的其他关键帧可分辨。 在一个实施例中,该方法和系统包括预处理源数字媒体以获得媒体帧的特征向量。 识别性地选择关键帧作为源数字媒体的每个片段的代表,其中所述鉴别选择包括确定每个候选关键帧的相似性度量,并且确定每个候选关键帧的不相似性度量,并且选择具有最高善计值计算的关键帧 相似和不相似的措施。