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    • 2. 发明申请
    • MATCHING TEXT TO IMAGES
    • 匹配文字到图像
    • US20120163707A1
    • 2012-06-28
    • US12979375
    • 2010-12-28
    • Simon BAKERDahua LINAnitha KannanQifa Ke
    • Simon BAKERDahua LINAnitha KannanQifa Ke
    • G06K9/62
    • G06K9/00456G06F17/2765G06F17/30265
    • Text in web pages or other text documents may be classified based on the images or other objects within the webpage. A system for identifying and classifying text related to an object may identify one or more web pages containing the image or similar images, determine topics from the text of the document, and develop a set of training phrases for a classifier. The classifier may be trained and then used to analyze the text in the documents. The training set may include both positive examples and negative examples of text taken from the set of documents. A positive example may include captions or other elements directly associated with the object, while negative examples may include text taken from the documents, but from a large distance from the object. In some cases, the system may iterate on the classification process to refine the results.
    • 可以基于网页内的图像或其他对象来对网页或其他文本文档中的文本进行分类。 用于识别和分类与对象相关的文本的系统可以识别包含图像或类似图像的一个或多个网页,从文档的文本确定主题,并且为分类器开发一组训练短语。 可以对分类器进行训练,然后用于分析文档中的文本。 训练集可能包括从该组文件中获取的文本的正面例子和否定的例子。 正面例子可以包括与对象直接相关联的标题或其他元素,而负面示例可以包括从文档中取出的文本,但是距离对象很远的距离。 在某些情况下,系统可能会对分类过程进行迭代以优化结果。
    • 4. 发明授权
    • Accurate text classification through selective use of image data
    • 通过选择性使用图像数据来准确地进行文本分类
    • US08768050B2
    • 2014-07-01
    • US13158484
    • 2011-06-13
    • Anitha KannanPartha Pratim TalukdarNikhil RasiwasiaQifa KeRakesh Agrawal
    • Anitha KannanPartha Pratim TalukdarNikhil RasiwasiaQifa KeRakesh Agrawal
    • G06K9/62
    • G06K9/6268G06K9/6227G06K9/6293
    • Product images are used in conjunction with textual descriptions to improve classifications of product offerings. By combining cues from both text and image descriptions associated with products, implementations enhance both the precision and recall of product description classifications within the context of web-based commerce search. Several implementations are directed to improving those areas where text-only approaches are most unreliable. For example, several implementations use image signals to complement text classifiers and improve overall product classification in situations where brief textual product descriptions use vocabulary that overlaps with multiple diverse categories. Other implementations are directed to using text and images “training sets” to improve automated classifiers including text-only classifiers. Certain implementations are also directed to learning a number of three-way image classifiers focused only on “confusing categories” of the text signals to improve upon those specific areas where text-only classification is weakest.
    • 产品图像与文本描述结合使用,以改进产品分类。 通过结合来自与产品相关的文本和图像描述的提示,实现在基于网络的商业搜索的上下文中增强了产品描述分类的精度和回收。 几个实现旨在改进那些仅文本方法最不可靠的领域。 例如,在简短的文本产品描述使用与多个不同类别重叠的词汇的情况下,多个实现使用图像信号来补充文本分类器并改进整体产品分类。 其他实现涉及使用文本和图像“训练集”来改进自动分类器,包括纯文本分类器。 某些实现也针对学习一些三维图像分类器,仅针对文本信号的“混淆类别”,以改善文本分类最弱的特定领域。
    • 5. 发明申请
    • ACCURATE TEXT CLASSIFICATION THROUGH SELECTIVE USE OF IMAGE DATA
    • 通过选择性使用图像数据的精确文本分类
    • US20120314941A1
    • 2012-12-13
    • US13158484
    • 2011-06-13
    • Anitha KannanPartha Pratim TalukdarNikhil RasiwasiaQifa KeRakesh Agrawal
    • Anitha KannanPartha Pratim TalukdarNikhil RasiwasiaQifa KeRakesh Agrawal
    • G06K9/62
    • G06K9/6268G06K9/6227G06K9/6293
    • Product images are used in conjunction with textual descriptions to improve classifications of product offerings. By combining cues from both text and image descriptions associated with products, implementations enhance both the precision and recall of product description classifications within the context of web-based commerce search. Several implementations are directed to improving those areas where text-only approaches are most unreliable. For example, several implementations use image signals to complement text classifiers and improve overall product classification in situations where brief textual product descriptions use vocabulary that overlaps with multiple diverse categories. Other implementations are directed to using text and images “training sets” to improve automated classifiers including text-only classifiers. Certain implementations are also directed to learning a number of three-way image classifiers focused only on “confusing categories” of the text signals to improve upon those specific areas where text-only classification is weakest.
    • 产品图像与文本描述结合使用,以改进产品分类。 通过结合来自与产品相关的文本和图像描述的提示,实现在基于网络的商业搜索的上下文中增强了产品描述分类的精度和回收。 几个实现旨在改进那些仅文本方法最不可靠的领域。 例如,在简短的文本产品描述使用与多个不同类别重叠的词汇的情况下,多个实现使用图像信号来补充文本分类器并改进整体产品分类。 其他实现涉及使用文本和图像训练集来改进自动分类器,包括纯文本分类器。 某些实现也针对学习一些三维图像分类器,仅针对混淆文本信号的类别,以改进文本分类最弱的特定区域。
    • 8. 发明授权
    • Partition min-hash for partial-duplicate image determination
    • 部分重复图像确定的分区最小散列
    • US08452106B2
    • 2013-05-28
    • US12729250
    • 2010-03-23
    • Qifa KeMichael A. IsardDavid Changsoo Lee
    • Qifa KeMichael A. IsardDavid Changsoo Lee
    • G06K9/66
    • G06K9/6202G06K9/4642
    • Images in a database or collection of images are each divided into multiple partitions with each partition corresponding to an area of an image. The partitions in an image may overlap with each other. Min-hash sketches are generated for each of the partitions and stored with the images. A user may submit an image and request that an image that is a partial match for the submitted image be located in the image collection. The submitted image is similarly divided into partitions and min-hash sketches are generated from the partitions. The min-hash sketches are compared with the stored min-hash sketches for matches, and images having partitions whose sketches are matches are returned as partial matching images.
    • 数据库或图像集合中的图像被分成多个分区,每个分区对应于图像的区域。 图像中的分区可能会彼此重叠。 为每个分区生成最小散列草图,并与图像一起存储。 用户可以提交图像并请求作为所提交图像的部分匹配的图像位于图像集合中。 提交的图像类似地划分为分区,并且从分区生成最小哈希草图。 将最小哈希草图与存储的最小哈希草图进行比较,并将具有其草图匹配的分区的图像作为部分匹配图像返回。
    • 9. 发明授权
    • User interface for three-dimensional navigation
    • 三维导航用户界面
    • US08276088B2
    • 2012-09-25
    • US11827530
    • 2007-07-11
    • Qifa KePeter E. HartJonathan J. HullHidenobu Kishi
    • Qifa KePeter E. HartJonathan J. HullHidenobu Kishi
    • G06F3/048
    • G06K9/2081G06K9/228G06K2209/01
    • The present invention uses invisible junctions which are a set of local features unique to every page of the electronic document to match the captured image to a part of an electronic document. The present invention includes: an image capture device, a feature extraction and recognition system and database. When an electronic document is printed, the feature extraction and recognition system captures an image of the document page. The features in the captured image are then extracted, indexed and stored in the database. Given a query image, usually a small patch of some document page captured by a low resolution image capture device, the features in the query image are extracted and compared against those stored in the database to identify the query image. The present invention also includes methods for recognizing and tracking the viewing region and look at point corresponding to the input query image. This information is combined with a rendering of the original input document to generate a new graphical user interface to the user. This user interface can be displayed on a conventional browser or even on the display of an image capture device.
    • 本发明使用作为电子文档的每一页特有的一组局部特征的不可见结,以将捕获的图像与电子文档的一部分相匹配。 本发明包括:图像捕获装置,特征提取和识别系统和数据库。 当打印电子文档时,特征提取和识别系统捕获文档页面的图像。 然后将捕获的图像中的特征提取,索引并存储在数据库中。 给定查询图像,通常是由低分辨率图像捕获设备捕获的一些文档页面的小补丁,提取查询图像中的特征并将其与存储在数据库中的特征进行比较以识别查询图像。 本发明还包括用于识别和跟踪观看区域并查看与输入查询图像相对应的点的方法。 该信息与原始输入文档的呈现相结合,以向用户生成新的图形用户界面。 该用户界面可以显示在常规浏览器上,甚至可以在图像捕获设备的显示器上显示。