Patents by Inventor Devi Parikh

Devi Parikh 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).

  • Efficiently identifying images, videos, songs or documents most relevant to the user based on attribute feedback
    Patent number: 9292517
    Abstract: A method, system and computer program product for efficiently identifying images, videos, audio files or documents relevant to a user. Using either manual annotations or learned functions, the method predicts the relative strength of an attribute in an image, video, audio file or document from a pool of images, videos, audio files or documents. At query time, the system presents an initial set of reference images, videos, audio files or documents, and the user selects among them to provide relative attribute feedback. Using the resulting constraints in the multi-dimensional attribute space, the relevance function for the pool of images, videos, audio files or documents is updated and the relevance of the pool of images, videos, audio files or documents is re-computed. This procedure iterates using the accumulated constraints until the top-ranked images, videos, audio files or documents are acceptably close to the user's envisioned image, video, audio file or document.
    Type: Grant
    Filed: August 13, 2013
    Date of Patent: March 22, 2016
    Assignee: Board of Regents, The University of Texas System
    Inventors: Kristen Grauman, Adriana Kovashka, Devi Parikh
  • EFFICIENTLY IDENTIFYING IMAGES, VIDEOS, SONGS OR DOCUMENTS MOST RELEVANT TO THE USER BASED ON ATTRIBUTE FEEDBACK
    Publication number: 20140188863
    Abstract: A method, system and computer program product for efficiently identifying images, videos, audio files or documents relevant to a user. Using either manual annotations or learned functions, the method predicts the relative strength of an attribute in an image, video, audio file or document from a pool of images, videos, audio files or documents. At query time, the system presents an initial set of reference images, videos, audio files or documents, and the user selects among them to provide relative attribute feedback. Using the resulting constraints in the multi-dimensional attribute space, the relevance function for the pool of images, videos, audio files or documents is updated and the relevance of the pool of images, videos, audio files or documents is re-computed. This procedure iterates using the accumulated constraints until the top-ranked images, videos, audio files or documents are acceptably close to the user's envisioned image, video, audio file or document.
    Type: Application
    Filed: August 13, 2013
    Publication date: July 3, 2014
    Applicant: Board of Regents, The University of Texas System
    Inventors: Kristen Grauman, Adriana Kovashka, Devi Parikh