Patents by Inventor Cordelia L. Schmid

Cordelia L. Schmid 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).

  • Label-embedding view of attribute-based recognition
    Patent number: 10331976
    Abstract: In image classification, each class of a set of classes is embedded in an attribute space where each dimension of the attribute space corresponds to a class attribute. The embedding generates a class attribute vector for each class of the set of classes. A set of parameters of a prediction function operating in the attribute space respective to a set of training images annotated with classes of the set of classes is optimized such that the prediction function with the optimized set of parameters optimally predicts the annotated classes for the set of training images. The prediction function with the optimized set of parameters is applied to an input image to generate at least one class label for the input image. The image classification does not include applying a class attribute classifier to the input image.
    Type: Grant
    Filed: June 21, 2013
    Date of Patent: June 25, 2019
    Assignee: XEROX CORPORATION
    Inventors: Zeynep Akata, Florent C. Perronnin, Zaid Harchaoui, Cordelia L. Schmid
  • LABEL-EMBEDDING VIEW OF ATTRIBUTE-BASED RECOGNITION
    Publication number: 20140376804
    Abstract: In image classification, each class of a set of classes is embedded in an attribute space where each dimension of the attribute space corresponds to a class attribute. The embedding generates a class attribute vector for each class of the set of classes. A set of parameters of a prediction function operating in the attribute space respective to a set of training images annotated with classes of the set of classes is optimized such that the prediction function with the optimized set of parameters optimally predicts the annotated classes for the set of training images. The prediction function with the optimized set of parameters is applied to an input image to generate at least one class label for the input image. The image classification does not include applying a class attribute classifier to the input image.
    Type: Application
    Filed: June 21, 2013
    Publication date: December 25, 2014
    Inventors: Zeynep Akata, Florent C. Perronnin, Zaid Harchaoui, Cordelia L. Schmid