Patents by Inventor Xidong Lin

Xidong Lin has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).

  • Method and device for comparing similarities of high dimensional features of images
    Patent number: 11048966
    Abstract: The present invention provides a method and device for comparing similarities of high dimensional features of images, capable of improving the retrieval speed and retrieval precision in a similarity retrieval from massive images based on Locality Sensitive HASH (LSH) code. The method for comparing similarities of high dimensional features of images according to the present invention comprises: reducing dimensions of extracted eigenvectors of the images by the LSH algorithm to obtain low dimensional eigenvectors; averagely segmenting the low dimensional eigenvectors and establishing a segment index table; retrieving the segmented low dimensional eigenvector of a queried image from the segment index table to obtain a candidate sample set; and performing a similarity metric between a sample in the candidate sample set and the low dimensional eigenvector of the queried image.
    Type: Grant
    Filed: July 13, 2016
    Date of Patent: June 29, 2021
    Assignees: BEIJING JINGDONG SHANGKE INFORMATION TECHNOLOGY CO., LTD., BEIJING JINGDONG CENTURY TRADING CO., LTD.
    Inventors: Xidong Lin, Chuan Mou
  • Method and Device for Comparing Similarities of High Dimensional Features of Images
    Publication number: 20180349735
    Abstract: The present invention provides a method and device for comparing similarities of high dimensional features of images, capable of improving the retrieval speed and retrieval precision in a similarity retrieval from massive images based on Locality Sensitive HASH (LSH) code. The method for comparing similarities of high dimensional features of images according to the present invention comprises: reducing dimensions of extracted eigenvectors of the images by the LSH algorithm to obtain low dimensional eigenvectors; averagely segmenting the low dimensional eigenvectors and establishing a segment index table; retrieving the segmented low dimensional eigenvector of a queried image from the segment index table to obtain a candidate sample set; and performing a similarity metric between a sample in the candidate sample set and the low dimensional eigenvector of the queried image.
    Type: Application
    Filed: July 13, 2016
    Publication date: December 6, 2018
    Inventors: Xidong Lin, Chuan Mou