IMAGE RECOGNITION METHOD AND APPARATUS, IMAGE VERIFICATION METHOD AND APPARATUS, LEARNING METHOD AND APPARATUS TO RECOGNIZE IMAGE, AND LEARNING METHOD AND APPARATUS TO VERIFY IMAGE
First Claim
1. A method of recognizing a feature of an image, the method comprising:
- receiving an input image including an object;
extracting first feature information using a first layer of a neural network, the first feature information indicating a first feature corresponding to the input image among a plurality of first features;
extracting second feature information using a second layer of the neural network, the second feature information indicating a second feature among a plurality of second features, the indicated second feature corresponding to the first feature information; and
recognizing an element corresponding to the object based on the first feature information and the second feature information.
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Abstract
A method of recognizing a feature of an image may include receiving an input image including an object; extracting first feature information using a first layer of a neural network, the first feature information indicating a first feature corresponding to the input image among a plurality of first features; extracting second feature information using a second layer of the neural network, the second feature information indicating a second feature among a plurality of second features, the indicated second feature corresponding to the first feature information; and recognizing an element corresponding to the object based on the first feature information and the second feature information.
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Citations
32 Claims
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1. A method of recognizing a feature of an image, the method comprising:
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receiving an input image including an object; extracting first feature information using a first layer of a neural network, the first feature information indicating a first feature corresponding to the input image among a plurality of first features; extracting second feature information using a second layer of the neural network, the second feature information indicating a second feature among a plurality of second features, the indicated second feature corresponding to the first feature information; and recognizing an element corresponding to the object based on the first feature information and the second feature information. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8)
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9. An apparatus for recognizing a feature of an image;
- the apparatus comprising;
a memory storing computer-readable instructions; and one or more processors configured to execute the computer-readable instructions such that the one or more processors are configured to, receive an input image including an object; extract first feature information using a first layer, the first feature information indicating a first feature among a plurality of first feature information, the indicated first feature corresponding to the input image; extract second feature information using a second layer, the second feature information indicating a second feature among a plurality of second features, the indicated second feature corresponding to the first feature information; and recognize an element corresponding to the object based on the first feature information and the second feature information. - View Dependent Claims (10, 11)
- the apparatus comprising;
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12. A method of learning a feature to recognize an image, the method comprising:
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receiving a training element and a training image associated with a training object; and learning a parameter of a recognizer such that the recognizer recognizes the training element from the training image, the recognizer being configured to recognize a plurality of elements from first feature information extracted from an input image using a first layer of a neural network and second feature information extracted using a second layer of the neural network. - View Dependent Claims (13, 14, 15, 16)
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17. An apparatus for learning a feature to recognize an image, the apparatus comprising:
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a memory storing computer-readable instructions; and one or more processors configured to execute the computer-readable instructions such that the one or more processors are configured to, receive a training element and a training image associated with a training object; implement a recognizer; and learn a parameter of the recognizer such that the recognizer recognizes the training element from the training image, the recognizer being configured to recognize a plurality of elements from first feature information extracted from an input image using a first layer of a neural network and second feature information extracted using a second layer of the neural network.
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18. A method of verifying a feature of an image, the method comprising:
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receiving a first image including a first object; extracting first feature information using a first layer of a neural network, the first feature information indicating a first feature among a plurality of first features, the indicated first feature corresponding to the first image; extracting second feature information using a second layer of the neural network, the second feature information indicating a second feature among a plurality of second features, the indicated second feature corresponding to the first feature information; and determining whether the object of the first image is similar to a second object of a second image, based on the first feature information and the second feature information of the first image and based on first feature information and second feature information associated with the second image. - View Dependent Claims (19, 20, 21, 22)
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23. An apparatus for verifying a feature of an image, the apparatus comprising:
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a memory storing computer-readable instructions; and one or more processors configured to execute the computer-readable instructions such that the one or more processors are configured to, receive a first image including a first object; extract first feature information using a first layer of a neural network, the first feature information indicating a first feature among a plurality of first features, the first feature corresponding to the first image; extract second feature information using a second layer of a neural network, the second feature information indicating a second feature among a plurality of second features, the second feature corresponding to the first feature information among; and determine whether the first object is similar to a second object of a second image, based on the first feature information and the second feature information of the first image and based on first feature information and second feature information associated with the second image. - View Dependent Claims (24, 25)
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26. A method of learning a feature to verify an image, the method comprising:
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receiving a pair of training images, and training information corresponding to the pair of training images; and learning a parameter of a verifier so that a result of comparing, by the verifier, the training images corresponds to the training information, the verifier being configured to determine whether two input images are similar to each other, based on first feature information extracted from the two input images using a first layer of a neural network and based on second feature information extracted using a second layer of the neural network. - View Dependent Claims (27, 28, 29, 30, 31)
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32. An apparatus for learning a feature to verify an image, the apparatus comprising:
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a memory storing computer-readable instructions; and one or more processors configured to execute the computer-readable instructions such that the one or more processors are configured to, receive a pair of training images, and training information corresponding to the pair of training images, and learn a parameter of a verifier so that a result of comparing, by the verifier, the training images corresponds to the training information, the verifier being configured to determine whether two input images are similar to each other, based on first feature information extracted from the two input images using a first layer of a neural network and based on second feature information extracted using a second layer of the neural network.
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Specification