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    LOGISTIC REGRESSION GRADIENT CALCULATION METHOD AND APPARATUS
    2.
    发明申请
    LOGISTIC REGRESSION GRADIENT CALCULATION METHOD AND APPARATUS 审中-公开

    公开(公告)号：US20180330191A1

    公开(公告)日：2018-11-15

    申请号：US16043043

    申请日：2018-07-23

    申请人： ALIBABA GROUP HOLDING LIMITED

    发明人： Jun ZHOU

    IPC分类号： G06K9/62 ,  G06F17/18 ,  G06Q30/02

    CPC分类号： G06K9/6221 ,  G06F17/18 ,  G06K9/6288 ,  G06Q30/0242 ,  G06Q30/0277

    摘要： The present disclosure provides logistic regression gradient calculation methods and apparatuses. One exemplary calculation method comprises: acquiring training data, the training data including X-row user data and Y-row click-through data corresponding to the X-row user data; converting the X-row user data into X-column data; segmenting the X-column data and a weight vector to form N X-column data segmentation blocks and N weight vector segmentation blocks; starting N threads respectively to generate N sub-logistic regression gradients according to the N X-column data segmentation blocks, the N weight vector segmentation blocks, and the corresponding Y-row click-through data; and splicing the N sub-logistic regression gradients to form a full logistic regression gradient. With embodiments of the present disclosure, a computing machine can support training of a super-large-scale logistic regression model, which increases the calculation speed, shortens the training time, and greatly reduces the memory usage of the computing machine.

    CLUSTERING IMAGES BASED ON CAMERA FINGERPRINTS
    3.
    发明申请
    CLUSTERING IMAGES BASED ON CAMERA FINGERPRINTS 审中-公开

    公开(公告)号：US20180253626A1

    公开(公告)日：2018-09-06

    申请号：US15757279

    申请日：2016-09-05

    申请人： Functional Technologies Ltd.

    发明人： Xufeng Lin ,  Chang-Tsun Li

    IPC分类号： G06K9/62 ,  G06K9/00

    CPC分类号： G06K9/6223 ,  G06K9/0051 ,  G06K9/00577 ,  G06K9/40 ,  G06K9/6218 ,  G06K9/6221 ,  G06K2009/00583

    摘要： A method of analysing a set of digital images each having been captured with a digital camera, the method comprising, using at least one processor: a) extracting a camera fingerprint from each image so as to form a set of camera fingerprints, each camera fingerprint being representative of the camera used to capture the image, and being of a first dimension; b) forming a set of dimensionally reduced camera fingerprints from each camera fingerprint, the dimensionally reduced camera fingerprint being of a second dimension smaller than the first dimension; c) forming a first subset of dimensionally reduced camera fingerprints and a second subset of dimensionally reduced camera fingerprints; d) determining a level of similarity between every pairing of the dimensionally reduced camera fingerprints of the first subset; e) determining a level of similarity between every pairing of the dimensionally reduced camera fingerprints of the second subset; f) determining a level of similarity between every pairing of, on the one hand, the dimensionally reduced fingerprints of the first set and, on the other hand, the dimensionally reduced fingerprints of the second subset; g) recording those pairings which indicate a comparatively higher level of similarity; h) substituting for the contents of the first subset those dimensionally reduced camera fingerprints of the first and second subsets which have been recorded as part of a pairing showing a comparatively higher level of similarity; i) substituting for the contents of the second subset a different subset of the set of the dimensionally reduced camera fingerprints; j) repeating steps (e) to (i), typically until all of the dimensionally reduced camera fingerprints have been processed; k) performing a clustering algorithm on all dimensionally reduced camera fingerprints based on the pairings having a comparatively higher level of similarity to produce a plurality of first clusters each comprising a set of dimensionally reduced camera fingerprints; l) for each of the first clusters, determining a level of similarity between each of the camera fingerprints corresponding to the dimensionally reduced camera fingerprints of that cluster; and m) splitting and merging the coarse clusters dependent upon the similarities between the camera fingerprints to form a plurality of second clusters.

    Anomalous object interaction detection and reporting
    6.
    发明授权
    Anomalous object interaction detection and reporting 有权

    公开(公告)号：US09911043B2

    公开(公告)日：2018-03-06

    申请号：US13931058

    申请日：2013-06-28

    申请人： Omni Al, Inc.

    发明人： Kishor Adinath Saitwal ,  Dennis G. Urech ,  Wesley Kenneth Cobb

    IPC分类号： G06T13/00 ,  G06K9/00 ,  G06K9/62

    CPC分类号： G06K9/00711 ,  G06K9/6221 ,  G06K9/6284

    摘要： Techniques are disclosed for analyzing a scene depicted in an input stream of video frames captured by a video camera. The techniques include evaluating sequence pairs representing segments of object trajectories. Assuming the objects interact, each of the sequences of the sequence pair may be mapped to a sequence cluster of an adaptive resonance theory (ART) network. A rareness value for the pair of sequence clusters may be determined based on learned joint probabilities of sequence cluster pairs. A statistical anomaly model, which may be specific to an interaction type or general to a plurality of interaction types, is used to determine an anomaly temperature, and alerts are issued based at least on the anomaly temperature. In addition, the ART network and the statistical anomaly model are updated based on the current interaction.

    METHODS AND APPARATUS TO EXTRACT TEXT FROM IMAGED DOCUMENTS
    7.
    发明申请
    METHODS AND APPARATUS TO EXTRACT TEXT FROM IMAGED DOCUMENTS 有权

    公开(公告)号：US20170124413A1

    公开(公告)日：2017-05-04

    申请号：US14927014

    申请日：2015-10-29

    申请人： The Nielsen Company (US), LLC

    发明人： Kevin Keqiang Deng

    IPC分类号： G06K9/34 ,  G06K9/64 ,  G06K9/03 ,  G06K9/62

    CPC分类号： G06K9/344 ,  G06K9/033 ,  G06K9/6221 ,  G06K9/64 ,  G06K2209/01

    摘要： Methods and apparatus to extract text from imaged documents are disclosed. Example methods include segmenting an image of a document into localized sub-images corresponding to individual characters in the document. The example methods further include grouping respective ones of the sub-images into a cluster based on a visual correlation of the respective ones of the sub-images to a reference sub-image. The visual correlation between the reference sub-image and the respective ones of the sub-images grouped into the cluster exceeding a correlation threshold. The example methods also include identifying a designated character for the cluster based on the sub-images grouped into the cluster. The example methods further include associating the designated character with locations in the image of the document associated with the respective ones of the sub-images grouped into the cluster.

    GROUPING FACE IMAGES USING STATISTIC DISTRIBUTION ESTIMATE
    9.
    发明申请
    GROUPING FACE IMAGES USING STATISTIC DISTRIBUTION ESTIMATE 有权
    使用统计分布估计对脸部图像进行分组

    公开(公告)号：US20150324630A1

    公开(公告)日：2015-11-12

    申请号：US14272809

    申请日：2014-05-08

    申请人： Shutterfly, Inc.

    发明人： Roman Sandler ,  Alexander M. Kenis

    IPC分类号： G06K9/00 ,  G06K9/52 ,  G06K9/62

    CPC分类号： G06K9/00228 ,  G06K9/00288 ,  G06K9/52 ,  G06K9/6215 ,  G06K9/6221 ,  G06K9/6267 ,  G06K9/66

    摘要： A computer-implemented method for sorting face images of different individuals into different groups includes obtaining face images comprising faces of unknown individuals by a computer processor; calculating similarity functions between pairs of face images by the computer processor; joining face images that have values of the similarity functions above a predetermined threshold into a hypothetical face group, wherein the face images in the hypothetical face group hypothetically belong to a same person; conducting non-negative matrix factorization on values of the similarity functions in the hypothetical face group to test truthfulness of the hypothetical face group; and identifying the hypothetical face group as a true face group if a percentage of the associated similarity functions being true is above a threshold based on the non-negative matrix factorization.

    摘要翻译： 用于将不同个体的面部图像分类为不同组的计算机实现的方法包括：通过计算机处理器获取包括未知个体的面部的面部图像; 通过计算机处理器计算人脸图像之间的相似度函数; 将具有高于预定阈值的相似度函数的面部图像连接到假想面部组中，其中假设面部组中的面部图像假设属于同一个人; 对假想面部组中相似度函数的值进行非负矩阵分解，以测试假想面部群体的真实性; 并且如果所述相关联的相似度函数的百分比为真，则将所述假想面部群体识别为真面团，基于所述非负矩阵因子分解，高于阈值。