公开(公告)号：US09424338B2

公开(公告)日：2016-08-23

申请号：US14264411

申请日：2014-04-29

Applicant: Google Inc.

Inventor： Yushi Jing ,  Michele Covell ,  Stephen C. Holiday

IPC: G06F17/30 ,  G06F15/16

CPC classification number: G06F17/30598 ,  G06F17/30256 ,  G06F17/3028 ,  G06F17/3053

Abstract: Aspects of the subject matter described herein relate to functions used for retrieving image results based on search queries. More specifically, image search queries can be pre-grouped or classified based on visual and semantic similarity. For example, a pairwise image similarity value for a pair of queries can be computed based on one or more of the sum of all of the overlapping the image results, the sum of the image distances between all of the pairs of images in the image results, and the rank of each of the images in the image results. The pairwise image similarity values can then be used to generate image query clusters. Each image query clusters can include a set of queries with high pairwise image similarity values. In some examples, a distance function can be determined for each image query cluster. This data can be used to provide image results.

Abstract translation: 本文描述的主题的方面涉及用于基于搜索查询来检索图像结果的功能。 更具体地，可以基于视觉和语义相似性对图像搜索查询进行预分组或分类。 例如，可以基于图像结果重叠的全部和之和中的一个或多个来计算一对查询的成对图像相似度值，图像结果中所有图像对之间的图像距离之和 ，以及图像中每个图像的等级。 然后可以使用成对图像相似度值来生成图像查询簇。 每个图像查询群集可以包括具有高成对图像相似度值的一组查询。 在一些示例中，可以为每个图像查询簇确定距离函数。 该数据可用于提供图像结果。

公开(公告)号：US09135271B1

公开(公告)日：2015-09-15

申请号：US13959597

申请日：2013-08-05

Applicant: Google Inc.

Inventor： Rohan Seth ,  Joshua J. Sacks ,  Michele Covell ,  Michael Chu ,  Shumeet Baluja ,  David Marwood

IPC: G06F7/00 ,  G06F17/30

CPC classification number: G06F17/30241 ,  G06F17/30864

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer-readable storage medium, for providing a method that comprises: determining excess queries over multiple time periods for a given geographic feature, where the geographic feature defines a location; comparing geographic features for similarity based at least in part on the excess queries associated with a respective geographic feature; and for a given target geographic feature, determining one or more similar geographic features based on the comparing.

Abstract translation: 方法，系统和装置，包括在计算机可读存储介质上编码的计算机程序，用于提供一种方法，该方法包括：确定给定地理特征的多个时间段的超量查询，其中所述地理特征定义位置; 至少部分地基于与相应地理特征相关联的多余查询来比较相似性的地理特征; 并且对于给定的目标地理特征，基于所述比较来确定一个或多个相似的地理特征。

公开(公告)号：US08914357B1

公开(公告)日：2014-12-16

申请号：US13913340

申请日：2013-06-07

Applicant: Google Inc.

Inventor： Rohan Seth ,  Joshua J. Sacks ,  Michele Covell ,  Michael Chu ,  Shumeet Baluja ,  David Marwood

IPC: G06F17/30

CPC classification number: G06F17/30477 ,  G06F17/3087

Abstract: Systems and methods are provided for mapping keywords to geographic features. In some aspects, a method includes identifying location keywords associated with granular locations and identifying geographic features associated with an area of interest that includes the granular locations. For each geographic feature, the method includes determining geo data for the geographic feature, forming a set of granular locations that is associated with the geographic feature using the determined geo data, and aggregating a set of location keywords from the identified location keywords. The set of location keywords is associated with the set of granular locations to form a keyword mapping for the geographic feature. The method includes receiving an indication of a geographic location associated with a user, determining a first geographic feature that includes the geographic location, and targeting content for delivery to the user using a corresponding keyword mapping for the determined first geographic feature.

Abstract translation: 提供了将关键字映射到地理特征的系统和方法。 在一些方面，一种方法包括识别与粒度位置相关联的位置关键字，并且识别与包括粒状位置的感兴趣区域相关联的地理特征。 对于每个地理特征，所述方法包括确定所述地理特征的地理数据，使用所确定的地理数据形成与所述地理特征相关联的一组细粒度位置，以及从所识别的位置关键词聚集一组位置关键词。 位置关键字集合与粒度位置集合相关联，以形成地理要素的关键字映射。 所述方法包括：接收与用户相关联的地理位置的指示，确定包括所述地理位置的第一地理特征，以及使用针对所确定的第一地理特征的相应关键字映射来定位用于传递给所述用户的内容。

公开(公告)号：US08787692B1

公开(公告)日：2014-07-22

申请号：US13946965

申请日：2013-07-19

Applicant: Google Inc.

Inventor： Mei Han ,  Michele Covell ,  Saurabh Mathur ,  Shumeet Baluja ,  Vivek Kwatra

IPC: G06K9/36

CPC classification number: G06K9/6219 ,  G06K9/6807 ,  H04N19/30 ,  H04N19/90

Abstract: An exemplar dictionary is built from example image blocks for determining predictor blocks for encoding and decoding images. The exemplar dictionary comprises a hierarchical organization of example image blocks. The hierarchical organization of image blocks is obtained by clustering a set of example image blocks, for example, based on k-means clustering. Performance of clustering is improved by transforming feature vectors representing the image blocks to fewer dimensions. Principal component analysis is used for determining feature vectors with fewer dimensions. The clustering performed at higher levels of the hierarchy uses fewer dimensions of feature vectors compared to lower levels of hierarchy. Performance of clustering is improved by processing only a sample of the image blocks of a cluster. The clustering performed at higher levels of the hierarchy uses lower sampling rates as compared to lower levels of hierarchy.

Abstract translation: 从用于确定用于对图像进行编码和解码的预测器块的示例图像块构建示范字典。 示例性字典包括示例图像块的分级组织。 通过例如基于k均值聚类来聚类一组示例图像块来获得图像块的分级组织。 通过将表示图像块的特征向量变换为较少的维度来提高聚类的性能。 主成分分析用于确定尺寸较小的特征向量。 在层次较高的层次上执行的聚类与较低级别的层次相比，使用较少的特征向量维度。 通过仅处理集群的图像块的样本来提高聚类的性能。 与较低级别的层次相比，在较高层次上执行的聚类使用较低的采样率。