公开(公告)号：US20180267996A1

公开(公告)日：2018-09-20

申请号：US15463757

申请日：2017-03-20

Applicant: ADOBE SYSTEMS INCORPORATED

Inventor： ZHE LIN ,  XIAOHUI SHEN ,  JIANMING ZHANG ,  HAILIN JIN ,  YINGWEI LI

IPC: G06F17/30 ,  G06T11/60 ,  G06T7/33 ,  G06K9/66 ,  G06N3/04 ,  G06N3/08

CPC classification number: G06F16/5866 ,  G06F16/532 ,  G06F16/951 ,  G06K9/00684 ,  G06K9/4628 ,  G06K9/4676 ,  G06K9/6248 ,  G06K9/6273 ,  G06K9/66 ,  G06N3/04 ,  G06N3/0454 ,  G06N3/08 ,  G06T7/33 ,  G06T11/60 ,  G06T2207/10016 ,  G06T2207/20081 ,  G06T2207/20084

Abstract: A framework is provided for associating dense images with topics. The framework is trained utilizing images, each having multiple regions, multiple visual characteristics and multiple keyword tags associated therewith. For each region of each image, visual features are computed from the visual characteristics utilizing a convolutional neural network, and an image feature vector is generated from the visual features. The keyword tags are utilized to generate a weighted word vector for each image by calculating a weighted average of word vector representations representing keyword tags associated with the image. The image feature vector and the weighted word vector are aligned in a common embedding space and a heat map is computed for the image. Once trained, the framework can be utilized to automatically tag images and rank the relevance of images with respect to queried keywords based upon associated heat maps.

公开(公告)号：US20180267997A1

公开(公告)日：2018-09-20

申请号：US15463769

申请日：2017-03-20

Applicant: ADOBE SYSTEMS INCORPORATED

Inventor： ZHE LIN ,  XIAOHUI SHEN ,  JIANMING ZHANG ,  HAILIN JIN ,  YINGWEI LI

IPC: G06F17/30 ,  G06T11/60 ,  G06T7/33 ,  G06K9/66 ,  G06N3/04 ,  G06N3/08

CPC classification number: G06F17/30268 ,  G06F17/30277 ,  G06F17/30864 ,  G06K9/00684 ,  G06K9/66 ,  G06N3/04 ,  G06N3/08 ,  G06T7/33 ,  G06T11/60 ,  G06T2207/10016 ,  G06T2207/20081 ,  G06T2207/20084

Abstract: A framework is provided for associating images with topics utilizing embedding learning. The framework is trained utilizing images, each having multiple visual characteristics and multiple keyword tags associated therewith. Visual features are computed from the visual characteristics utilizing a convolutional neural network and an image feature vector is generated therefrom. The keyword tags are utilized to generate a weighted word vector (or “soft topic feature vector”) for each image by calculating a weighted average of word vector representations that represent the keyword tags associated with the image. The image feature vector and the soft topic feature vector are aligned in a common embedding space and a relevancy score is computed for each of the keyword tags. Once trained, the framework can automatically tag images and a text-based search engine can rank image relevance with respect to queried keywords based upon predicted relevancy scores.