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公开(公告)号：US20120148149A1
公开(公告)日：2012-06-14
申请号：US12964778
申请日：2010-12-10
申请人： Mrityunjay Kumar , Jie Yu , Alexander C. Loui
发明人： Mrityunjay Kumar , Jie Yu , Alexander C. Loui
CPC分类号： G06K9/00711 , G06K9/6244
摘要： A method for identifying a set of key frames from a video sequence including a time sequence of video frames, comprising: extracting a feature vector for each video frame in a set of video frames selected from the video sequence; defining a set of basis functions that can be used to represent the extracted feature vectors, wherein each basis function is associated with a different video frame in the set of video frames; representing the feature vectors for each video frame in the set of video frames as a sparse combination of the basis functions associated with the other video frames; and analyzing the sparse combinations of the basis functions for the set of video frames to select the set of key frames.
摘要翻译： 一种用于从包括视频帧的时间序列的视频序列中识别一组关键帧的方法，包括：提取从所述视频序列中选择的一组视频帧中的每个视频帧的特征向量; 定义可用于表示所提取的特征向量的一组基函数，其中每个基函数与视频帧集合中的不同视频帧相关联; 将所述视频帧集合中的每个视频帧的特征向量表示为与其它视频帧相关联的基本函数的稀疏组合; 以及分析视频帧集合的基本函数的稀疏组合以选择该组关键帧。
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公开(公告)号：US09076043B2
公开(公告)日：2015-07-07
申请号：US13565926
申请日：2012-08-03
IPC分类号： H04N5/445 , G06K9/00 , G11B27/034 , G11B27/28 , G06K9/62
CPC分类号： G06K9/00765 , G06K9/6249 , G11B27/034 , G11B27/28
摘要： A method for identifying a set of key video frames from a video sequence comprising extracting feature vectors for each video frame and applying a group sparsity algorithm to represent the feature vector for a particular video frame as a group sparse combination of the feature vectors for the other video frames. Weighting coefficients associated with the group sparse combination are analyzed to determine video frame clusters of temporally-contiguous, similar video frames. A summary is formed based on the determined video frame clusters.
摘要翻译： 一种用于从视频序列中识别一组关键视频帧的方法，包括提取每个视频帧的特征向量，并且应用组稀疏算法来表示特定视频帧的特征向量作为另一个的特征向量的组稀疏组合 视频帧。 分析与组稀疏组合相关联的加权系数，以确定时间上相邻的类似视频帧的视频帧聚类。 基于确定的视频帧聚类形成摘要。
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公开(公告)号：US08401292B2
公开(公告)日：2013-03-19
申请号：US13094217
申请日：2011-04-26
申请人： Minwoo Park , Alexander C. Loui , Mrityunjay Kumar
发明人： Minwoo Park , Alexander C. Loui , Mrityunjay Kumar
IPC分类号： G06K9/34
CPC分类号： G06K9/4671 , G06K9/34 , G06T7/11 , G06T7/162 , G06T2207/20072 , G06T2207/20164
摘要： A method for identifying high saliency regions in a digital image, comprising: segmenting the digital image into a plurality of segmented regions; determining a saliency value for each segmented region, merging neighboring segmented regions that share a common boundary in response to determining that one or more specified merging criteria are satisfied; and designating one or more of the segmented regions to be high saliency regions. The determination of the saliency value for a segmented region includes: determining a surround region including a set of image pixels surrounding the segmented region; analyzing the image pixels in the segmented region to determine one or more segmented region attributes; analyzing the image pixels in the surround region to determine one or more corresponding surround region attributes; determining a region saliency value responsive to differences between the one or more segmented region attributes and the corresponding surround region attributes.
摘要翻译： 一种用于识别数字图像中的高显着区域的方法，包括：将所述数字图像分割成多个分割区域; 确定每个分段区域的显着值，以响应于确定满足一个或多个指定的合并标准来合并共享公共边界的相邻分割区域; 并且将一个或多个分割区域指定为高显着区域。 分割区域的显着性值的确定包括：确定围绕分割区域的一组图像像素的环绕区域; 分析分割区域中的图像像素以确定一个或多个分段区域属性; 分析环绕区域中的图像像素以确定一个或多个相应的环绕区域属性; 响应于所述一个或多个分段区域属性和对应的环绕区域属性之间的差异来确定区域显着值。
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公开(公告)号：US08989503B2
公开(公告)日：2015-03-24
申请号：US13565919
申请日：2012-08-03
IPC分类号： G06K9/48
CPC分类号： G06K9/00765 , G06K9/00 , G06K9/00751 , G06K9/48 , G06K9/6249 , G06K2009/4695
摘要： A method for identifying a set of key video frames from a video sequence comprising extracting feature vectors for each video frame and applying a group sparsity algorithm to represent the feature vector for a particular video frame as a group sparse combination of the feature vectors for the other video frames. Weighting coefficients associated with the group sparse combination are analyzed to determine video frame clusters of temporally-contiguous, similar video frames. The video sequence is segmented into scenes by identifying scene boundaries based on the determined video frame clusters.
摘要翻译： 一种用于从视频序列中识别一组关键视频帧的方法，包括提取每个视频帧的特征向量，并且应用组稀疏算法来表示特定视频帧的特征向量作为另一个的特征向量的组稀疏组合 视频帧。 分析与组稀疏组合相关联的加权系数，以确定时间上相邻的类似视频帧的视频帧聚类。 通过基于确定的视频帧聚类识别场景边界，将视频序列分割成场景。
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公开(公告)号：US08976299B2
公开(公告)日：2015-03-10
申请号：US13413982
申请日：2012-03-07
IPC分类号： H04N7/50 , G11B27/28 , H04N5/14 , H04N21/44 , H04N21/8549
CPC分类号： H04N5/144 , G11B27/28 , H04N21/44008 , H04N21/8549
摘要： A method for determining a scene boundary location dividing a first scene and a second scene in an input video sequence. The scene boundary location is determined responsive to a merit function value, which is a function of the candidate scene boundary location. The merit function value for a particular candidate scene boundary location is determined by representing the dynamic scene content for the input video frames before and after candidate scene boundary using sparse combinations of a set of basis functions, wherein the sparse combinations of the basis functions are determined by finding a sparse vector of weighting coefficients for each of the basis functions. The weighting coefficients determined for each of the input video frames are combined to determine the merit function value. The candidate scene boundary providing the smallest merit function value is designated to be the scene boundary location.
摘要翻译： 一种用于确定在输入视频序列中划分第一场景和第二场景的场景边界位置的方法。 响应于作为候选场景边界位置的函数的优值函数值来确定场景边界位置。 通过使用一组基函数的稀疏组合表示候选场景边界之前和之后的输入视频帧的动态场景内容来确定特定候选场景边界位置的优值函数值，其中确定基函数的稀疏组合 通过找出每个基本函数的加权系数的稀疏矢量。 为每个输入视频帧确定的加权系数被组合以确定优值函数值。 提供最小优值函数值的候选场景边界被指定为场景边界位置。
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公开(公告)号：US20130235275A1
公开(公告)日：2013-09-12
申请号：US13413982
申请日：2012-03-07
IPC分类号： H04N5/14
CPC分类号： H04N5/144 , G11B27/28 , H04N21/44008 , H04N21/8549
摘要： A method for determining a scene boundary location dividing a first scene and a second scene in an input video sequence. The scene boundary location is determined responsive to a merit function value, which is a function of the candidate scene boundary location. The merit function value for a particular candidate scene boundary location is determined by representing the dynamic scene content for the input video frames before and after candidate scene boundary using sparse combinations of a set of basis functions, wherein the sparse combinations of the basis functions are determined by finding a sparse vector of weighting coefficients for each of the basis functions. The weighting coefficients determined for each of the input video frames are combined to determine the merit function value. The candidate scene boundary providing the smallest merit function value is designated to be the scene boundary location.
摘要翻译： 一种用于确定在输入视频序列中划分第一场景和第二场景的场景边界位置的方法。 响应于作为候选场景边界位置的函数的优值函数值确定场景边界位置。 通过使用一组基函数的稀疏组合表示候选场景边界之前和之后的输入视频帧的动态场景内容来确定特定候选场景边界位置的优值函数值，其中确定基函数的稀疏组合 通过找出每个基本函数的加权系数的稀疏矢量。 为每个输入视频帧确定的加权系数被组合以确定优值函数值。 提供最小优值函数值的候选场景边界被指定为场景边界位置。
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公开(公告)号：US20120275701A1
公开(公告)日：2012-11-01
申请号：US13094217
申请日：2011-04-26
申请人： Minwoo Park , Alexander C. Loui , Mrityunjay Kumar
发明人： Minwoo Park , Alexander C. Loui , Mrityunjay Kumar
IPC分类号： G06K9/34
CPC分类号： G06K9/4671 , G06K9/34 , G06T7/11 , G06T7/162 , G06T2207/20072 , G06T2207/20164
摘要： A method for identifying high saliency regions in a digital image, comprising: segmenting the digital image into a plurality of segmented regions; determining a saliency value for each segmented region, merging neighboring segmented regions that share a common boundary in response to determining that one or more specified merging criteria are satisfied; and designating one or more of the segmented regions to be high saliency regions. The determination of the saliency value for a segmented region includes: determining a surround region including a set of image pixels surrounding the segmented region; analyzing the image pixels in the segmented region to determine one or more segmented region attributes; analyzing the image pixels in the surround region to determine one or more corresponding surround region attributes; determining a region saliency value responsive to differences between the one or more segmented region attributes and the corresponding surround region attributes.
摘要翻译： 一种用于识别数字图像中的高显着区域的方法，包括：将所述数字图像分割成多个分割区域; 确定每个分段区域的显着值，以响应于确定满足一个或多个指定的合并标准来合并共享公共边界的相邻分割区域; 并且将一个或多个分割区域指定为高显着区域。 分割区域的显着性值的确定包括：确定围绕分割区域的一组图像像素的环绕区域; 分析分割区域中的图像像素以确定一个或多个分段区域属性; 分析环绕区域中的图像像素以确定一个或多个相应的环绕区域属性; 响应于所述一个或多个分段区域属性和对应的环绕区域属性之间的差异来确定区域显着值。
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公开(公告)号：US08913835B2
公开(公告)日：2014-12-16
申请号：US13565911
申请日：2012-08-03
IPC分类号： G06K9/48
CPC分类号： G06K9/48 , G06K9/00718 , G06K9/00744 , G06K9/00751 , G06K9/00765 , G06K9/6224
摘要： A method for identifying a set of key video frames from a video sequence comprising extracting feature vectors for each video frame and applying a group sparsity algorithm to represent the feature vector for a particular video frame as a group sparse combination of the feature vectors for the other video frames. Weighting coefficients associated with the group sparse combination are analyzed to determine video frame clusters of temporally-contiguous, similar video frames. A set of key video frames are selected based on the determined video frame clusters.
摘要翻译： 一种用于从视频序列中识别一组关键视频帧的方法，包括提取每个视频帧的特征向量，并且应用组稀疏算法来表示特定视频帧的特征向量作为另一个的特征向量的组稀疏组合 视频帧。 分析与组稀疏组合相关联的加权系数，以确定时间上相邻的类似视频帧的视频帧聚类。 基于所确定的视频帧集群来选择一组关键视频帧。
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公开(公告)号：US20140037269A1
公开(公告)日：2014-02-06
申请号：US13565926
申请日：2012-08-03
IPC分类号： G11B27/00
CPC分类号： G06K9/00765 , G06K9/6249 , G11B27/034 , G11B27/28
摘要： A method for identifying a set of key video frames from a video sequence comprising extracting feature vectors for each video frame and applying a group sparsity algorithm to represent the feature vector for a particular video frame as a group sparse combination of the feature vectors for the other video frames. Weighting coefficients associated with the group sparse combination are analyzed to determine video frame clusters of temporally-contiguous, similar video frames. A summary is formed based on the determined video frame clusters.
摘要翻译： 一种用于从视频序列中识别一组关键视频帧的方法，包括提取每个视频帧的特征向量，并且应用组稀疏算法来表示特定视频帧的特征向量作为另一个的特征向量的组稀疏组合 视频帧。 分析与组稀疏组合相关联的加权系数，以确定时间上相邻的类似视频帧的视频帧聚类。 基于确定的视频帧聚类形成摘要。
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公开(公告)号：US20140037215A1
公开(公告)日：2014-02-06
申请号：US13565911
申请日：2012-08-03
IPC分类号： G06K9/48
CPC分类号： G06K9/48 , G06K9/00718 , G06K9/00744 , G06K9/00751 , G06K9/00765 , G06K9/6224
摘要： A method for identifying a set of key video frames from a video sequence comprising extracting feature vectors for each video frame and applying a group sparsity algorithm to represent the feature vector for a particular video frame as a group sparse combination of the feature vectors for the other video frames. Weighting coefficients associated with the group sparse combination are analyzed to determine video frame clusters of temporally-contiguous, similar video frames. A set of key video frames are selected based on the determined video frame clusters.
摘要翻译： 一种用于从视频序列中识别一组关键视频帧的方法，包括提取每个视频帧的特征向量，并且应用组稀疏算法来表示特定视频帧的特征向量作为另一个的特征向量的组稀疏组合 视频帧。 分析与组稀疏组合相关联的加权系数，以确定时间上相邻的类似视频帧的视频帧聚类。 基于所确定的视频帧集群来选择一组关键视频帧。
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