NOVEL CRITERIA FOR GAUSSIAN MIXTURE MODEL CLUSTER SELECTION IN SCALABLE COMPRESSED FISHER VECTOR (SCFV) GLOBAL DESCRIPTOR
    11.
    发明申请
    NOVEL CRITERIA FOR GAUSSIAN MIXTURE MODEL CLUSTER SELECTION IN SCALABLE COMPRESSED FISHER VECTOR (SCFV) GLOBAL DESCRIPTOR 审中-公开
    GAUSSIAN混合模型集群选择标准压缩渔船矢量(SCFV)全球描述符的新标准

    公开(公告)号:US20140198998A1

    公开(公告)日:2014-07-17

    申请号:US14151657

    申请日:2014-01-09

    CPC classification number: G06F16/5838

    Abstract: A wireless communication device includes a processor configured to execute an image query. The image query utilizes cluster selection criteria for a cluster-aggregation based vectorization of a set of local features based on a quantity of top local features having the highest posteriori probability values. The cluster selection criterion is measured as the summation of the posteriori probability values of the top local features. The quantity of top local features is determined by a predetermined integer value greater than one.

    Abstract translation: 无线通信设备包括被配置为执行图像查询的处理器。 基于具有最高后验概率值的顶部局部特征的量,图像查询利用基于聚类聚集的集群选择标准来矢量化一组局部特征。 集群选择标准被测量为顶部局部特征的后验概率值的总和。 顶部本地特征的数量由大于1的预定整数值确定。

    ROBUST KEYPOINT FEATURE SELECTION FOR VISUAL SEARCH WITH SELF MATCHING SCORE
    12.
    发明申请
    ROBUST KEYPOINT FEATURE SELECTION FOR VISUAL SEARCH WITH SELF MATCHING SCORE 有权
    强大的键盘特征选择,用于自拍匹配的视觉搜索

    公开(公告)号:US20140185941A1

    公开(公告)日:2014-07-03

    申请号:US14101047

    申请日:2013-12-09

    CPC classification number: G06K9/6211 G06K9/4671

    Abstract: To improve feature selection accuracy during a visual search, interest points within a query image are two-way matched to features in an affine transformed image or otherwise transformed version of the query image. A user device implements a method for selecting local descriptors in the visual search. The method includes: detecting a first set of interest points for the original image; computing an affine transform matrix; computing a new image as a transformation of the original image using the affine transform matrix; detecting a second set of interest points from the and new image; performing a two-way matching between the first set of interest points and the second set of interest points; sorting matching pairs according to a specified self-matching score (SMS); assigning an infinite value to SMS of unmatched interest points from the original image; selecting the interest points based on SMS. Significant performance gains reduce false positive matches.

    Abstract translation: 为了在视觉搜索期间提高特征选择精度,查询图像内的兴趣点与仿射变换图像中的特征或查询图像的其他变换版本进行双向匹配。 用户设备实现在视觉搜索中选择本地描述符的方法。 该方法包括:检测原始图像的第一组兴趣点; 计算仿射变换矩阵; 使用仿射变换矩阵计算新图像作为原始图像的变换; 从新图像检测第二组兴趣点; 执行第一组兴趣点与第二组兴趣点之间的双向匹配; 根据指定的自匹配分数(SMS)对匹配对进行排序; 从原始图像向无缝匹配的兴趣点的SMS分配无限值; 基于短信选择兴趣点。 显着的业绩增益减少假阳性匹配。

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