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    • 2. 发明授权
    • System and method for computing the visual profile of a place
    • 用于计算场所视觉轮廓的系统和方法
    • US09298982B2
    • 2016-03-29
    • US13190753
    • 2011-07-26
    • Florent PerronninNicolas GuérinCraig John Saunders
    • Florent PerronninNicolas GuérinCraig John Saunders
    • G06K9/00
    • G06K9/00476
    • A system and method for computing a place profile are disclosed. The method includes providing a geographical definition of a place, retrieving a set of images based on the geographical place definition. With a classifier, image-level statistics for the retrieved images are generated. The classifier has been trained to generate image-level statistics for a finite set of classes, such as different activities. The image-level statistics are aggregated to generate a place profile for the defined place which may be displayed to a user who has provided information for generating the geographical definition or used in an application such as a recommender system or to generate a personal profile for the user.
    • 公开了一种用于计算地点轮廓的系统和方法。 该方法包括提供地点的地理定义,基于地理位置定义检索一组图像。 使用分类器,生成检索到的图像的图像级统计信息。 已经对分类器进行了训练,以生成有限集合类(例如不同活动)的图像级统计信息。 聚集图像级统计信息以生成可以向已经提供用于生成地理定义的信息的用户显示的定义的地点的位置简档,或者在诸如推荐器系统之类的应用中使用的位置简档,或者为 用户。
    • 8. 发明授权
    • System and method for object class localization and semantic class based image segmentation
    • 用于对象类定位和基于语义类的图像分割的系统和方法
    • US08111923B2
    • 2012-02-07
    • US12191579
    • 2008-08-14
    • Gabriela CsurkaFlorent Perronnin
    • Gabriela CsurkaFlorent Perronnin
    • G06K9/46G06K9/00G06K9/64
    • G06K9/00624G06K9/342G06K9/4676G06T7/11G06T2207/10024G06T2207/20021G06T2207/30236G06T2207/30252
    • An automated image processing system and method are provided for class-based segmentation of a digital image. The method includes extracting a plurality of patches of an input image. For each patch, at least one feature is extracted. The feature may be a high level feature which is derived from the application of a generative model to a representation of low level feature(s) of the patch. For each patch, and for at least one object class from a set of object classes, a relevance score for the patch, based on the at least one feature, is computed. For at least some or all of the pixels of the image, a relevance score for the at least one object class based on the patch scores is computed. An object class is assigned to each of the pixels based on the computed relevance score for the at least one object class, allowing the image to be segmented and the segments labeled, based on object class.
    • 提供了一种用于数字图像的基于分类的分割的自动图像处理系统和方法。 该方法包括提取输入图像的多个片段。 对于每个补丁,至少提取一个要素。 该特征可以是从生成模型的应用导出到补丁的低级特征的表示的高级特征。 对于每个补丁以及来自一组对象类的至少一个对象类,基于至少一个特征来计算补丁的相关性得分。 对于图像的至少一些或全部像素,计算基于补丁得分的至少一个对象类别的相关度得分。 基于所计算的至少一个对象类的相关性分数,将对象类分配给每个像素,允许根据对象类来分割图像和标记的图像。
    • 9. 发明申请
    • FAST ALGORITHM FOR CONVEX OPTIMIZATION WITH APPLICATION TO DENSITY ESTIMATION AND CLUSTERING
    • 用于突变优化的快速算法应用于密度估计和聚类
    • US20100088073A1
    • 2010-04-08
    • US12245939
    • 2008-10-06
    • Florent PerronninGuillaume Bouchard
    • Florent PerronninGuillaume Bouchard
    • G06F17/10
    • G06F17/11G06K9/6222G06K9/6226
    • A method of maximizing a concave log-likelihood function comprises: selecting a pair of parameters from a plurality of adjustable parameters of a concave log-likelihood function; maximizing a value of the concave log-likelihood function respective to an adjustment value to generate an optimal adjustment value, wherein the value of one member of the selected pair of parameters is increased by the adjustment value and the value of the other member of the selected pair of parameters is decreased by the adjustment value; updating values of the plurality of adjustable parameters by increasing the value of the one member of the selected pair of parameters by the optimized adjustment value and decreasing the value of the other member of the selected pair of parameters by the optimized adjustment value; and repeating the selecting, maximizing, and updating for different pairs of parameters to identify optimized values of the plurality of adjustable parameters.
    • 最大化凹对数似然函数的方法包括:从凹对数似然函数的多个可调参数中选择一对参数; 最大化相应于调整值的凹对数似然函数的值以生成最佳调整值,其中所选择的一对参数中的一个成员的值被增加了所选择的另一成员的调整值和值 一对参数减少调整值; 通过优化的调整值增加所选择的一对参数中的一个成员的值并通过优化的调整值减小所选择的一对参数中的另一个成员的值来更新多个可调参数的值; 以及重复对不同参数对的选择,最大化和更新以识别所述多​​个可调参数的优化值。