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Learning image categorization using related attributes

  • US 9,953,425 B2
  • Filed: 07/30/2014
  • Issued: 04/24/2018
  • Est. Priority Date: 07/30/2014
  • Status: Active Grant
First Claim
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1. A non-transitory computer storage medium comprising computer-useable instructions that, when used by one or more computing devices, cause the one or more computing devices to perform operations comprising:

  • implementing a regularized double-column convolutional neural network (RDCNN) to classify image features for a set of images, the implementing comprising;

    receiving an image from the set of images;

    training a first feature column in a first neural network of the RDCNN using a first set of image representations as inputs to the first feature column, the trained first feature column having fixed parameters;

    generating a first set of image attributes by the trained first feature column using the fixed parameters; and

    training a second feature multi-column in a second neural network of the RDCNN using the generated first set of image attributes, wherein the parameters of the trained first feature column remain fixed, wherein the second feature multi-column comprises at least two columns that are independent in each convolutional layer of the second feature multi-column, andwherein each of the at least two columns receives a different input; and

    identifying a class associated with the second feature multi-column for the image using the implemented RDCNN.

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