公开(公告)号：US10296846B2

公开(公告)日：2019-05-21

申请号：US14950544

申请日：2015-11-24

Applicant: Xerox Corporation

Inventor： Gabriela Csurka ,  Boris Chidlovskii ,  Stéphane Clinchant

IPC: G06N3/04 ,  G06N3/08 ,  G06N5/02 ,  G06N20/00

Abstract: A domain-adapted classification system and method are disclosed. The method includes mapping an input set of representations to generate an output set of representations, using a learned transformation. The input set of representations includes a set of target samples from a target domain. The input set also includes, for each of a plurality of source domains, a class representation for each of a plurality of classes. The class representations are representative of a respective set of source samples from the respective source domain labeled with a respective class. The output set of representations includes an adapted representation of each of the target samples and an adapted class representation for each of the classes for each of the source domains. A class label is predicted for at least one of the target samples based on the output set of representations and information based on the predicted class label is output.

公开(公告)号：US20170220897A1

公开(公告)日：2017-08-03

申请号：US15013273

申请日：2016-02-02

Applicant: Xerox Corporation

Inventor： Boris Chidlovskii ,  Gabriela Csurka ,  Stéphane Clinchant

IPC: G06K9/62 ,  G06N99/00

CPC classification number: G06N99/005 ,  G06K9/00684 ,  G06K9/6256 ,  G06K9/6262 ,  G06K9/6298

Abstract: A machine learning method operates on training instances from a plurality of domains including one or more source domains and a target domain. Each training instance is represented by values for a set of features. Domain adaptation is performed using stacked marginalized denoising autoencoding (mSDA) operating on the training instances to generate a stack of domain adaptation transform layers. Each iteration of the domain adaptation includes corrupting the training instances in accord with feature corruption probabilities that are non-uniform over at least one of the set of features and the domains. A classifier is learned on the training instances transformed using the stack of domain adaptation transform layers. Thereafter, a label prediction is generated for an input instance from the target domain represented by values for the set of features by applying the classifier to the input instance transformed using the stack of domain adaptation transform domains.

公开(公告)号：US20160078359A1

公开(公告)日：2016-03-17

申请号：US14504837

申请日：2014-10-02

Applicant: Xerox Corporation

Inventor： Gabriela Csurka ,  Boris Chidlovskii ,  Florent C. Perronnin

IPC: G06N7/00 ,  G06N99/00

CPC classification number: G06N7/005 ,  G06K9/6215 ,  G06K9/6262 ,  G06K9/6272 ,  G06N20/00

Abstract: A classification system includes memory which stores, for each of a set of classes, a classifier model for assigning a class probability to a test sample from a target domain. The classifier model has been learned with training samples from the target domain and from at least one source domain. Each classifier model models the respective class as a mixture of components, the component mixture including a component for each source domain and a component for the target domain. Each component is a function of a distance between the test sample and a domain-specific class representation which is derived from the training samples of the respective domain that are labeled with the class, each of the components in the mixture being weighted by a respective mixture weight. Instructions, implemented by a processor, are provided for labeling the test sample based on the class probabilities assigned by the classifier models.

Abstract translation: 分类系统包括存储器，用于针对一组类别中的每一个存储用于将类概率分配给来自目标域的测试样本的分类器模型。 已经通过来自目标域和至少一个源域的训练样本学习了分类器模型。 每个分类器模型将各个类作为组件的混合模型，组件混合包括每个源域的组件和目标域的组件。 每个组件是测试样本与由类别标记的各个域的训练样本派生的领域特定类别表示之间的距离的函数，混合物中的每个组分由相应的混合物加权 重量。 提供由处理器实施的指令，用于根据分类器模型分配的类概率标记测试样本。

公开(公告)号：US20150346326A1

公开(公告)日：2015-12-03

申请号：US14287538

申请日：2014-05-27

Applicant: Xerox Corporation

Inventor： Boris Chidlovskii ,  Gabriela Csurka ,  Jose Antonio Rodriguez Serrano

IPC: G01S7/48 ,  G01S17/88 ,  G01S17/08

CPC classification number: G01S17/88 ,  G01S7/4802 ,  G01S17/42 ,  G01S17/89 ,  G06K9/00805 ,  G06K9/6206 ,  G06T7/00 ,  G06T7/30 ,  G06T2207/10028

Abstract: A system and method for classifying vehicles from laser scan data by receiving laser scan data corresponding to multiple vehicles from a laser scanner; extracting vehicle shapes corresponding to the multiple vehicles based on the laser scan data; aligning the vehicle shapes; and generating vehicle profiles based on the aligned vehicle shapes. The system and method can further include aligning the vehicle shapes using sequence kernels, such as global alignment kernels, and constraining the sequence kernels based on determined weights.

Abstract translation: 一种用于通过从激光扫描仪接收对应于多个车辆的激光扫描数据来对车辆进行激光扫描数据分类的系统和方法; 基于所述激光扫描数据提取与所述多个车辆相对应的车辆形状; 对准车辆形状; 以及基于对齐的车辆形状产生车辆轮廓。 系统和方法还可以包括使用诸如全局对准内核之类的序列内核来对齐车辆形状，并且基于确定的权重来约束序列内核。

公开(公告)号：US20150149203A1

公开(公告)日：2015-05-28

申请号：US14087128

申请日：2013-11-22

Applicant: Xerox Corporation

Inventor： Gabriela Csurka ,  Florent C. Perronnin ,  Mario Agustin Ricardo Jarmasz

IPC: G06F19/00 ,  G06Q10/10

CPC classification number: G16H10/60 ,  G06Q10/109 ,  G06Q10/1095

Abstract: A medical event tracking system includes memory which stores instructions for detecting new records in a personal health record of a single individual, identifying one of the new records that refers to an event for which an action is to be performed by at least one of the individual and a person acting on behalf of the individual, generating a candidate calendar entry for the identified new record which proposes a time for the action to be performed by the at least one of the individual and the person acting on behalf of the individual, providing for receiving validation of the candidate calendar entry, and providing for sending the validated candidate calendar entry to a personal electronic calendar of at least one of the individual and the person acting on behalf of the individual. A processor in communication with the memory executes the instructions.

Abstract translation: 医疗事件跟踪系统包括存储器，用于存储用于检测单个人的个人健康记录中的新记录的指令，识别参考由个体中的至少一个执行动作的事件的新记录之一 以及代表个人行事的人，为所识别的新记录产生候选日历条目，该候选日历条目提出了由个人和代表个人行事的人中的至少一个执行该诉讼的时间，规定 接收候选日历条目的验证，以及提供将验证的候选日历条目发送到个人和代表个人行事的个人中的至少一个的个人电子日历。 与存储器通信的处理器执行指令。

公开(公告)号：US20140132976A1

公开(公告)日：2014-05-15

申请号：US13677341

申请日：2012-11-15

Applicant: XEROX CORPORATION

Inventor： Gabriela Csurka ,  Jutta K. Willamowski ,  Yves Hoppenot

IPC: G06K15/02

CPC classification number: G06F3/1219 ,  G06F3/126 ,  G06F3/1273 ,  G06F3/1274 ,  G06F3/1288

Abstract: A method and system for managing print jobs is disclosed. A received print job is compared with pending print jobs and executed print jobs, wherein the pending print jobs and the executed print jobs are stored in one or more print queues associated with one or more printing systems. Thereafter, one or more pending print jobs are suspended if the one or more pending print jobs are found similar to the received print job based on the comparison; or the received print job is suspended if the received print job is found similar to one or more of the executed print jobs, based on the comparison.

Abstract translation: 公开了一种用于管理打印作业的方法和系统。 将接收的打印作业与待处理的打印作业和执行的打印作业进行比较，其中待处理打印作业和执行的打印作业存储在与一个或多个打印系统相关联的一个或多个打印队列中。 此后，如果基于该比较发现与接收到的打印作业类似的一个或多个挂起的打印作业，则暂停一个或多个挂起的打印作业; 或者如果接收的打印作业被发现类似于所执行的打印作业中的一个或多个，则基于该比较，所接收的打印作业被暂停。

公开(公告)号：US10289909B2

公开(公告)日：2019-05-14

申请号：US15450620

申请日：2017-03-06

Applicant: Xerox Corporation

Inventor： Fabien Baradel ,  Boris Chidlovskii ,  Gabriela Csurka

IPC: G06K9/00 ,  G06K9/46 ,  G06K9/62

Abstract: A method and apparatus for classifying an image. In one example, the method may include receiving one or more images associated with a source domain and one or more images associated with a target domain, identifying one or more source domain features based on the one or more images associated with the source domain, identifying one or more target domain features based on the one or more images associated with the target domain, training a conditional maximum mean discrepancy (CMMD) engine based on a difference between the one or more source domain features and the one or more target domain features, applying the CMMD engine to the one or more images associated with the target domain to generate one or more labels for each unlabeled target image of the one or more images associated with the target domain and classifying each one of the one or more images in the target domain using the one or more labels.

公开(公告)号：US20180024968A1

公开(公告)日：2018-01-25

申请号：US15216805

申请日：2016-07-22

Applicant: Xerox Corporation

Inventor： Stéphane Clinchant ,  Gabriela Csurka ,  Boris Chidlovskii

IPC: G06F17/14 ,  G06N7/00 ,  G06N99/00

CPC classification number: G06N3/0454

Abstract: A method for domain adaptation of samples includes receiving training samples from a plurality of domains, the plurality of domains including at least one source domain and a target domain, each training sample including values for a set of features. A domain predictor is learned on at least some of the training samples from the plurality of domains and respective domain labels. Domain adaptation is performed on the training samples using marginalized denoising autoencoding. This generates a domain adaptation transform layer (or layers) that transforms the training samples to a common adapted feature space. The domain adaptation employs the domain predictor to bias the domain adaptation towards one of the plurality of domains. Domain adapted training samples and their class labels can be used to train a classifier for prediction of class labels for unlabeled target samples that have been domain adapted with the domain adaptation transform layer(s).

公开(公告)号：US20160070986A1

公开(公告)日：2016-03-10

申请号：US14477215

申请日：2014-09-04

Applicant: Xerox Corporation

Inventor： Boris Chidlovskii ,  Gabriela Csurka

IPC: G06K9/62 ,  G06K9/00

CPC classification number: G06K9/6259 ,  G06K9/00624 ,  G06K9/00771 ,  G06K9/00785 ,  G06K9/46 ,  G06K9/62 ,  G06K9/6217 ,  G06K9/6256 ,  G06K9/6257 ,  G06K9/6267 ,  G06K9/627 ,  G06K9/6288

Abstract: In camera-based object labeling, boost classifier ƒT(x)=Σr=1Mβrhr(x) is trained to classify an image represented by feature vector x using a target domain training set DT of labeled feature vectors representing images acquired by the same camera and a plurality of source domain training sets DS1, . . . , DSN acquired by other cameras. The training applies an adaptive boosting (AdaBoost) algorithm to generate base classifiers hr(x) and weights βr. The rth iteration of the AdaBoost algorithm trains candidate base classifiers hrk(x) each trained on a training set DT∪DSk, and selects hr(x) from previously trained candidate base classifiers. The target domain training set DT may be expanded based on a prior estimate of the labels distribution for the target domain. The object labeling system may be a vehicle identification system, a machine vision article inspection system, or so forth.

Abstract translation: 在基于相机的对象标签中，训练增强分类器ƒT（x）=＆Sgr; r = 1M＆bgr; rhr（x）以使用特征向量x表示的图像来分类由特征向量x表示的图像的标记特征向量的目标域训练集DT， 相同的摄像机和多个源域训练集DS1，。 。 。 ，其他相机收购的DSN。 训练采用自适应增强（AdaBoost）算法生成基本分类器hr（x）和权重bgr; r。 AdaBoost算法的第r次迭代训练在训练集DT∪DSK上训练的候选基类分类器hrk（x），并从先前训练过的候选基类分类器中选择hr（x）。 可以基于目标域的标签分布的先前估计来扩展目标域训练集DT。 物体标识系统可以是车辆识别系统，机器视觉物品检查系统等。

公开(公告)号：US20150046119A1

公开(公告)日：2015-02-12

申请号：US13963472

申请日：2013-08-09

Applicant: Xerox Corporation

Inventor： Harsimrat Singh Sandhawalia ,  Jose Antonio Rodriguez Serrano ,  Herve Poirier ,  Gabriela Csurka

IPC: G01B11/24

CPC classification number: G01B11/24 ,  G01B11/2518 ,  G06K9/0014 ,  G06K9/00147 ,  G06K9/00187 ,  G06K9/00214 ,  G06K9/4676 ,  G06K9/54 ,  G06K9/6247

Abstract: Methods, systems and processor-readable media for vehicle classification. In general, one or more vehicles can be scanned utilizing a laser scanner to compile data indicative of an optical profile of the vehicle(s). The optical profile associated with the vehicle(s) is then pre-processed. Particular features are extracted from the optical profile following pre-processing of the optical profile. The vehicle(s) can be then classified based on the particular features extracted from the optical feature. A segmented laser profile is treated as an image and profile features that integrate the signal in one of the two directions of the image and Fisher vectors which aggregate statistics of local “patches” of the image are computed and utilized as part of the extraction and classification process.

Abstract translation: 用于车辆分类的方法，系统和处理器可读介质。 通常，可以利用激光扫描器扫描一个或多个车辆，以编译指示车辆的光学轮廓的数据。 然后与车辆相关联的光学轮廓被预处理。 在光学轮廓的预处理之后，从光学轮廓提取特定特征。 然后可以基于从光学特征提取的特定特征对车辆进行分类。 分割的激光轮廓被视为图像和轮廓特征，其将信号整合在图像的两个方向中的一个方向上，并且汇总图像的局部“斑块”的统计信息的Fisher向量被计算并用作提取和分类的一部分 处理。