公开(公告)号：US20150030238A1

公开(公告)日：2015-01-29

申请号：US13953394

申请日：2013-07-29

Applicant: Adobe Systems Incorporated

Inventor： Jianchao Yang ,  Guang Chen ,  Jonathan Brandt ,  Hailin Jin ,  Elya Shechtman ,  Aseem Omprakash Agarwala

IPC: G06K9/62

CPC classification number: G06K9/627 ,  G06K9/4642

Abstract: A system may be configured as an image recognition machine that utilizes an image feature representation called local feature embedding (LFE). LFE enables generation of a feature vector that captures salient visual properties of an image to address both the fine-grained aspects and the coarse-grained aspects of recognizing a visual pattern depicted in the image. Configured to utilize image feature vectors with LFE, the system may implement a nearest class mean (NCM) classifier, as well as a scalable recognition algorithm with metric learning and max margin template selection. Accordingly, the system may be updated to accommodate new classes with very little added computational cost. This may have the effect of enabling the system to readily handle open-ended image classification problems.

Abstract translation: 系统可以被配置为利用称为局部特征嵌入（LFE）的图像特征表示的图像识别机器。 LFE能够生成捕获图像的显着视觉特性的特征向量，以解决识别图像中描绘的视觉图案的细粒度方面和粗粒度方面。 配置为利用具有LFE的图像特征向量，系统可以实现最近的等级均值（NCM）分类器，以及具有度量学习和最大边距模板选择的可缩放识别算法。 因此，可以更新系统以容纳新类别，而且增加了很少的计算成本。 这可能具有使系统能够容易地处理开放式图像分类问题的效果。

公开(公告)号：US09501724B1

公开(公告)日：2016-11-22

申请号：US14734466

申请日：2015-06-09

Applicant: ADOBE SYSTEMS INCORPORATED

Inventor： Jianchao Yang ,  Zhangyang Wang ,  Jonathan Brandt ,  Hailin Jin ,  Elya Shechtman ,  Aseem Omprakash Agarwala

IPC: G06K9/00 ,  G06K9/36 ,  G06K9/66 ,  G06K9/62 ,  G06K9/68 ,  G06T3/40

CPC classification number: G06T3/40 ,  G06K9/6255 ,  G06K9/6828

Abstract: A convolutional neural network (CNN) is trained for font recognition and font similarity learning. In a training phase, text images with font labels are synthesized by introducing variances to minimize the gap between the training images and real-world text images. Training images are generated and input into the CNN. The output is fed into an N-way softmax function dependent on the number of fonts the CNN is being trained on, producing a distribution of classified text images over N class labels. In a testing phase, each test image is normalized in height and squeezed in aspect ratio resulting in a plurality of test patches. The CNN averages the probabilities of each test patch belonging to a set of fonts to obtain a classification. Feature representations may be extracted and utilized to define font similarity between fonts, which may be utilized in font suggestion, font browsing, or font recognition applications.

Abstract translation: 对卷积神经网络（CNN）进行字体识别和字体相似学习。 在训练阶段，通过引入差异来合成具有字体标签的文本图像，以最小化训练图像与真实世界文本图像之间的差距。 生成训练图像并将其输入到CNN中。 根据CNN正在训练的字体数量，输出被输入到N-way softmax函数中，产生N类标签上分类文本图像的分布。 在测试阶段，每个测试图像的高度被标准化，并以纵横比挤压，从而产生多个测试贴片。 CNN对属于一组字体的每个测试补丁的概率进行平均，以获得分类。 可以提取和利用特征表示来定义可以在字体建议，字体浏览或字体识别应用中使用的字体之间的字体相似性。

公开(公告)号：US09141885B2

公开(公告)日：2015-09-22

申请号：US13953394

申请日：2013-07-29

Applicant: Adobe Systems Incorporated

Inventor： Jianchao Yang ,  Guang Chen ,  Jonathan Brandt ,  Hailin Jin ,  Elya Shechtman ,  Aseem Omprakash Agarwala

IPC: G06K9/00 ,  G06K9/62

CPC classification number: G06K9/627 ,  G06K9/4642

Abstract: A system may be configured as an image recognition machine that utilizes an image feature representation called local feature embedding (LFE). LFE enables generation of a feature vector that captures salient visual properties of an image to address both the fine-grained aspects and the coarse-grained aspects of recognizing a visual pattern depicted in the image. Configured to utilize image feature vectors with LFE, the system may implement a nearest class mean (NCM) classifier, as well as a scalable recognition algorithm with metric learning and max margin template selection. Accordingly, the system may be updated to accommodate new classes with very little added computational cost. This may have the effect of enabling the system to readily handle open-ended image classification problems.

Abstract translation: 系统可以被配置为利用称为局部特征嵌入（LFE）的图像特征表示的图像识别机器。 LFE能够生成捕获图像的显着视觉特性的特征向量，以解决识别图像中描绘的视觉图案的细粒度方面和粗粒度方面。 配置为利用具有LFE的图像特征向量，系统可以实现最近的等级均值（NCM）分类器，以及具有度量学习和最大边距模板选择的可缩放识别算法。 因此，可以更新系统以容纳新类别，而且增加了很少的计算成本。 这可能具有使系统能够容易地处理开放式图像分类问题的效果。

公开(公告)号：US09524449B2

公开(公告)日：2016-12-20

申请号：US14107191

申请日：2013-12-16

Applicant: Adobe Systems Incorporated

Inventor： Jianchao Yang ,  Guang Chen ,  Hailin Jin ,  Jonathan Brandt ,  Elya Shechtman ,  Aseem Omprakash Agarwala

IPC: G06K9/62 ,  G06K9/68

CPC classification number: G06K9/6267 ,  G06K9/6219 ,  G06K9/6282 ,  G06K9/6807

Abstract: Example systems and methods for classifying visual patterns into a plurality of classes are presented. Using reference visual patterns of known classification, at least one image or visual pattern classifier is generated, which is then employed to classify a plurality of candidate visual patterns of unknown classification. The classification scheme employed may be hierarchical or nonhierarchical. The types of visual patterns may be fonts, human faces, or any other type of visual patterns or images subject to classification.

Abstract translation: 提出了将视觉模式分类为多个类的示例系统和方法。 使用已知分类的参考视觉图案，生成至少一个图像或视觉模式分类器，然后将其用于对未知分类的多个候选视觉图案进行分类。 所使用的分类方案可以是分层的或非分层的。 视觉图案的类型可以是字体，人脸或任何其他类型的可分类的视觉图案或图像。

公开(公告)号：US20150170000A1

公开(公告)日：2015-06-18

申请号：US14107191

申请日：2013-12-16

Applicant: Adobe Systems Incorporated

Inventor： Jianchao Yang ,  Guang Chen ,  Hailin Jin ,  Jonathan Brandt ,  Elya Shechtman ,  Aseem Omprakash Agarwala

IPC: G06K9/62

CPC classification number: G06K9/6267 ,  G06K9/6219 ,  G06K9/6282 ,  G06K9/6807

Abstract: Example systems and methods for classifying visual patterns into a plurality of classes are presented. Using reference visual patterns of known classification, at least one image or visual pattern classifier is generated, which is then employed to classify a plurality of candidate visual patterns of unknown classification. The classification scheme employed may be hierarchical or nonhierarchical. The types of visual patterns may be fonts, human faces, or any other type of visual patterns or images subject to classification.

Abstract translation: 提出了将视觉模式分类为多个类的示例系统和方法。 使用已知分类的参考视觉图案，生成至少一个图像或视觉模式分类器，然后将其用于对未知分类的多个候选视觉图案进行分类。 所使用的分类方案可以是分层的或非分层的。 视觉图案的类型可以是字体，人脸或任何其他类型的可分类的视觉图案或图像。