CPC G06F 18/22 (2023.01) [G06F 3/14 (2013.01); G06F 18/2431 (2023.01); G06F 18/253 (2023.01); G06F 18/40 (2023.01); G06N 3/04 (2013.01); G06N 3/08 (2013.01); G06T 7/0002 (2013.01); G06V 10/40 (2022.01); G06V 20/46 (2022.01); G06T 2207/10016 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/30168 (2013.01)] | 20 Claims |
1. An electronic device, comprising:
circuitry configured to:
receive a first image;
extract, by a Deep Neural Network (DNN) model, a first set of image features associated with the received first image;
generate a first feature vector associated with the received first image based on the extracted first set of image features;
extract, by an image-feature detection model, a second set of image features associated with the received first image;
generate a second feature vector associated with the received first image based on the extracted second set of image features;
determine, based on a resolution of the received first image, each of:
a first weight associated with the generated first feature vector, and
a second weight associated with the generated second feature vector;
combine the generated first feature vector and the generated second feature vector based on the determined first weight and the determined second weight;
generate a third feature vector associated with the received first image based on the combination of the generated first feature vector and the generated second feature vector;
determine a similarity metric between the generated third feature vector associated with the received first image and a fourth feature vector of each of a set of pre-stored second images;
identify a pre-stored third image from the set of pre-stored second images based on the determined similarity metric; and
control a display device to display information associated with the identified pre-stored third image.
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