Abstract: A user terminal apparatus is provided. The user terminal apparatus includes a camera configured to obtain a captured image; a storage configured to store a food intake history; a processor configured to extract a food image from the captured image and determine a food type of a food item included in the captured image based on feature information of the extracted food image and the previously stored food intake history; and a display configured to display relevant information about the determined food type.

Abstract: An electronic apparatus is provided. The electronic apparatus includes a camera configured to obtain a user image by capturing an image of a user, a memory configured to store one or more instructions, and a processor configured to execute the one or more instructions stored in the memory, wherein the processor is further configured to, by executing the one or more instructions, recognize the user from a face region of the user image by using a first recognition model learned based on face information of a plurality of users, extract additional feature information regarding the recognized user from the user image, allow the first recognition model to additionally learn based on the extracted additional feature information, recognize the user from a person region of the user image by using an additionally learned second recognition model, and output a recognition result of the second recognition model.

Abstract: A method for controlling an electronic device including at least one processor configured to encrypt an image and upload the encrypted image to an external server by using an artificial intelligence neural network model is provided. The method includes receiving a command to upload an image to the external server; acquiring, based on the command, a characteristic value corresponding to the image by inputting the image and a key of the electronic device into a neural network model trained to identify characteristic values based on an input image and an input key; and transmitting identification information of the image and the characteristic value to the external server.

Abstract: An electronic apparatus includes a processor configured to obtain a plurality of images, extract deep features with respect to the plurality of images using a feature extraction model, classify the plurality of images into certain groups using the extracted deep features and a classification model, display a result of the classification on the display, determine whether the feature extraction model and/or the classification model need to be updated using the result of the classification, and train and update at least one of the feature extraction model and the classification model based on a result of the determination. The electronic apparatus may estimate a deep feature of an image using a rule-based or artificial intelligence (AI) algorithm. When the deep feature of the image is estimated using the AI algorithm, the electronic apparatus may use a machine learning, neural network, or deep learning algorithm, or the like.

Abstract: A user terminal apparatus is provided. The user terminal apparatus includes a camera configured to obtain a captured image; a storage configured to store a food intake history; a processor configured to extract a food image from the captured image and determine a food type of a food item included in the captured image based on feature information of the extracted food image and the previously stored food intake history; and a display configured to display relevant information about the determined food type.

Abstract: A user terminal apparatus is provided. The user terminal apparatus includes a camera configured to obtain a captured image; a storage configured to store a food intake history; a processor configured to extract a food image from the captured image and determine a food type of a food item included in the captured image based on feature information of the extracted food image and the previously stored food intake history; and a display configured to display relevant information about the determined food type.

Abstract: A method for controlling an electronic device including at least one processor configured to encrypt an image and upload the encrypted image to an external server by using an artificial intelligence neural network model is provided. The method includes receiving a command to upload an image to the external server; acquiring, based on the command, a characteristic value corresponding to the image by inputting the image and a key of the electronic device into a neural network model trained to identify characteristic values based on an input image and an input key; and transmitting identification information of the image and the characteristic value to the external server.

Abstract: An electronic apparatus is provided. The electronic apparatus includes a camera configured to obtain a user image by capturing an image of a user, a memory configured to store one or more instructions, and a processor configured to execute the one or more instructions stored in the memory, wherein the processor is further configured to, by executing the one or more instructions, recognize the user from a face region of the user image by using a first recognition model learned based on face information of a plurality of users, extract additional feature information regarding the recognized user from the user image, allow the first recognition model to additionally learn based on the extracted additional feature information, recognize the user from a person region of the user image by using an additionally learned second recognition model, and output a recognition result of the second recognition model.

Abstract: A method for controlling an electronic device including at least one processor configured to encrypt an image and upload the encrypted image to an external server by using an artificial intelligence neural network model is provided. The method includes receiving a command to upload an image to the external server; acquiring, based on the command, a characteristic value corresponding to the image by inputting the image and a key of the electronic device into a neural network model trained to identify characteristic values based on an input image and an input key; and transmitting identification information of the image and the characteristic value to the external server.

Abstract: An electronic apparatus is provided. The electronic apparatus includes a camera configured to obtain a user image by capturing an image of a user, a memory configured to store one or more instructions, and a processor configured to execute the one or more instructions stored in the memory, wherein the processor is further configured to, by executing the one or more instructions, recognize the user from a face region of the user image by using a first recognition model learned based on face information of a plurality of users, extract additional feature information regarding the recognized user from the user image, allow the first recognition model to additionally learn based on the extracted additional feature information, recognize the user from a person region of the user image by using an additionally learned second recognition model, and output a recognition result of the second recognition model.

Abstract: A user terminal apparatus is provided. The user terminal apparatus includes a camera configured to obtain a captured image; a storage configured to store a food intake history; a processor configured to extract a food image from the captured image and determine a food type of a food item included in the captured image based on feature information of the extracted food image and the previously stored food intake history; and a display configured to display relevant information about the determined food type.

Abstract: A user terminal apparatus is provided. The user terminal apparatus includes a camera configured to obtain a captured image; a storage configured to store a food intake history; a processor configured to extract a food image from the captured image and determine a food type of a food item included in the captured image based on feature information of the extracted food image and the previously stored food intake history; and a display configured to display relevant information about the determined food type.

Abstract: A user terminal apparatus is provided. The user terminal apparatus includes a camera configured to obtain a captured image; a storage configured to store a food intake history; a processor configured to extract a food image from the captured image and determine a food type of a food item included in the captured image based on feature information of the extracted food image and the previously stored food intake history; and a display configured to display relevant information about the determined food type.

Abstract: A user terminal apparatus is provided. The user terminal apparatus includes a camera configured to obtain a captured image; a storage configured to store a food intake history; a processor configured to extract a food image from the captured image and determine a food type of a food item included in the captured image based on feature information of the extracted food image and the previously stored food intake history; and a display configured to display relevant information about the determined food type.

Abstract: A method for controlling an electronic device including at least one processor configured to encrypt an image and upload the encrypted image to an external server by using an artificial intelligence neural network model is provided. The method includes receiving a command to upload an image to the external server; acquiring, based on the command, a characteristic value corresponding to the image by inputting the image and a key of the electronic device into a neural network model trained to identify characteristic values based on an input image and an input key; and transmitting identification information of the image and the characteristic value to the external server.

Abstract: An electronic apparatus includes a processor configured to obtain a plurality of images, extract deep features with respect to the plurality of images using a feature extraction model, classify the plurality of images into certain groups using the extracted deep features and a classification model, display a result of the classification on the display, determine whether the feature extraction model and/or the classification model need to be updated using the result of the classification, and train and update at least one of the feature extraction model and the classification model based on a result of the determination. The electronic apparatus may estimate a deep feature of an image using a rule-based or artificial intelligence (AI) algorithm. When the deep feature of the image is estimated using the AI algorithm, the electronic apparatus may use a machine learning, neural network, or deep learning algorithm, or the like.

Abstract: An electronic apparatus is provided. The electronic apparatus includes a camera configured to obtain a user image by capturing an image of a user, a memory configured to store one or more instructions, and a processor configured to execute the one or more instructions stored in the memory, wherein the processor is further configured to, by executing the one or more instructions, recognize the user from a face region of the user image by using a first recognition model learned based on face information of a plurality of users, extract additional feature information regarding the recognized user from the user image, allow the first recognition model to additionally learn based on the extracted additional feature information, recognize the user from a person region of the user image by using an additionally learned second recognition model, and output a recognition result of the second recognition model.

Abstract: A user terminal apparatus is provided. The user terminal apparatus includes a camera configured to obtain a captured image; a storage configured to store a food intake history; a processor configured to extract a food image from the captured image and determine a food type of a food item included in the captured image based on feature information of the extracted food image and the previously stored food intake history; and a display configured to display relevant information about the determined food type.

Abstract: A method executed by a server to provide a mobile device with relevant information about an object in a captured image received from an image reproducing device, the method including: receiving, from the image reproducing device, an image captured by the image reproducing device; receiving, from the mobile device, information corresponding to an application usage history of the mobile device; searching, based on the application usage history, for the relevant information about the object in the captured image; and transmitting to the mobile device the relevant information found based on the searching.

Abstract: A method executed by a server to provide a mobile device with relevant information about an object in a captured image received from an image reproducing device, the method including: receiving, from the image reproducing device, an image captured by the image reproducing device; receiving, from the mobile device, information corresponding to an application usage history of the mobile device; searching, based on the application usage history, for the relevant information about the object in the captured image; and transmitting to the mobile device the relevant information found based on the searching.