Patent Publication Number: US-2023156317-A1

Title: Electronic device for obtaining image at user-intended moment and method for controlling the same

Description:
CROSS REFERENCE TO RELATED APPLICATION 
     This application is a PCT-Bypass Continuation of International Application No. PCT/KR2022/017475 designating the United States, filed on Nov. 8, 2022, in the Korean Intellectual Property Receiving Office and claiming priority to 10-2021-0179133, filed on Dec. 14, 2021, and Korean Patent Application No. 10-2021-0158293, filed on Nov. 17, 2021, the disclosures of all of which are incorporated by reference herein in their entireties. 
    
    
     BACKGROUND 
     Technical Field 
     Various embodiments of the disclosure relate to an electronic device for obtaining an image at a user-intended moment and a method for controlling the same. 
     Description of Related Art 
     An artificial intelligence (AI) system is a computer system that realizes human-level intelligence, and is a system in which a machine autonomously learns, makes a judgment, and becomes smarter unlike an existing rule-based smart system. As the AI system is used, the AI system improves in recognition rate and more accurately understands users preferences, and thus existing rule-based smart systems are gradually being replaced by deep learning-based AI systems. 
     Artificial intelligence technology includes machine learning (deep learning) and element technologies using machine learning. 
     Machine learning is an algorithm technology for autonomously categorizing/learning characteristics of input data, and an element technology is a technology for simulating a function, such as cognition and judgment, of a human brain using a machine learning algorithm, such as deep learning, and includes technical fields, such as linguistic understanding, visual understanding, inference/prediction, knowledge expression, and operation control. 
     Artificial intelligence technology can be applied to one or more fields, such as the following examples. Linguistic understanding is a technology for recognizing and applying/processing human language/text, and includes natural language processing, machine translation, dialogue system, question and answer, and speech recognition/synthesis. Visual understanding is a technology for recognizing and processing an object like human vision, and includes object recognition, object tracking, image search, human recognition, scene understanding, spatial understanding, image enhancement, and the like. Inference and prediction is a technology for making logical inference and prediction by judging information, and includes knowledge/probability-based inference, optimization prediction, preference-based planning, suggestion, and the like. Knowledge expression is a technology for automatically processing human experience information into knowledge data, and includes knowledge construction (data generation/classification) and knowledge management (data utilization). Operation control is a technology for controlling autonomous driving of a vehicle and movement of a robot, and includes motion control (navigation, collision, and driving), manipulation control (action control), and the like. 
     Many image obtaining operations using a camera depend on a user&#39;s manipulation. However, it is difficult for the user to input a manipulation for capture at a moment the user intends to capture the image, and a delay after a manipulation input and shaking at the manipulation input makes it difficult to obtain a high-quality image at an intended moment. 
     Although there is a preview or a function of capturing an image satisfying a specific condition while taking a video, this function also relies on the user to input a user manipulation to take a video at a moment the user intends to capture the video. 
     An AI-driven autosuggestion technology (e.g., single-take mode) for capturing an image while taking a video using artificial intelligence suggests an image to a user, based on a criterion, such as no blurring, good color, and good composition, predetermined by a developer but has difficulty reflecting each user&#39;s taste. 
     The disclosure relates to an electronic device for obtaining an image at a user-intended moment and a method for controlling the same. 
     SUMMARY 
     According to an embodiment, an electronic device may include a camera, a memory, and a processor, wherein the processor may receive information about an image capturing situation from a user, may obtain a target image based on the information about the image capturing situation using a first artificial intelligence module stored in the memory, may obtain feature information included in the target image using a second artificial intelligence module stored in the memory, may store the feature information in the memory, and may store an image related to the feature information in the memory when obtaining the image related to the feature information through the camera. 
     According to an embodiment, a method for controlling an electronic device may include receiving information about an image capturing situation from a user, obtaining a target image based on the information about the image capturing situation using a first artificial intelligence module stored in a memory, obtaining feature information included in the target image using a second artificial intelligence module stored in the memory, storing the feature information in the memory, and storing an image related to the feature information in the memory when obtaining the image related to the feature information through a camera. 
     According to an embodiment, a method may include: capturing a video in a single-take mode, the single take mode collecting a plurality of frames for a predetermined time, compressing the plurality of frames into low-dimensional vectors, comparing feature information for each of the plurality of frames, wherein the feature information is obtained from a reference database based on information about an image capturing situation input from a user, extracting at least one frame including a feature stored in the reference database among the plurality of frames collected for the predetermined time, and storing the extracted at least one frame or suggesting the extracted at least one frame to the user. 
     An electronic device according to various embodiments of the disclosure may obtain an image at a user-desired moment and may provide the image for a user. 
     An electronic device according to various embodiments of the disclosure may obtain an image to be provided for a user in further consideration of user-desired feature information without affecting a pipeline of an existing single-take mode. 
    
    
     
       BRIEF DESCRIPTION OF DRAWING 
         FIG.  1    is a block diagram of an electronic device in a network environment according to various embodiments; 
         FIG.  2    is a flowchart illustrating an operation of an electronic device obtaining an image at a user-intended moment according to an embodiment; 
         FIG.  3    illustrates an operation of an electronic device storing feature information about a user-intended moment according to an embodiment; 
         FIG.  4    illustrates an operation of an electronic device obtaining a target image at a user-intended moment according to an embodiment; 
         FIG.  5 A  illustrates an operation of training an artificial intelligence module to obtain a target image at a user-intended moment of an electronic device according to an embodiment; 
         FIG.  5 B  illustrates an operation of training an artificial intelligence module to obtain feature information about a user-intended moment of an electronic device according to an embodiment; 
         FIG.  5 C  illustrates an operation of training an artificial intelligence module to obtain feature information about a user-intended moment of an electronic device, according to an embodiment; 
         FIG.  6    illustrates an operation of obtaining feature information about a user-intended moment, based on the user&#39;s voice, according to an embodiment; 
         FIG.  7    illustrates an operation of obtaining an image or a video at a user-intended moment in a single-take mode according to an embodiment; 
         FIG.  8    illustrates an operation of obtaining an image at a user-intended moment in a single-take mode according to an embodiment; 
         FIG.  9    illustrates an operation of obtaining an image and a video at a user-intended moment in a single-take mode according to an embodiment; 
         FIG.  10    illustrates an operation of obtaining an image and a video at a user-intended moment in a single-take mode according to an embodiment; 
         FIG.  11    illustrates an operation of obtaining an image and a video at a user-intended moment through a preview of a camera application according to an embodiment; and 
         FIG.  12    illustrates an operation of obtaining an image and a video at a user-intended moment through a preview of a camera application according to an embodiment. 
     
    
    
     DETAILED DESCRIPTION 
       FIG.  1    is a block diagram illustrating an electronic device  101  in a network environment  100  according to various embodiments. Referring to  FIG.  1   , the electronic device  101  in the network environment  100  may communicate with an electronic device  102  via a first network  198  (e.g., a short-range wireless communication network), or at least one of an electronic device  104  or a server  108  via a second network  199  (e.g., a long-range wireless communication network). According to an embodiment, the electronic device  101  may communicate with the electronic device  104  via the server  108 . According to an embodiment, the electronic device  101  may include a processor  120 , memory  130 , an input module  150 , a sound output module  155 , a display module  160 , an audio module  170 , a sensor module  176 , an interface  177 , a connecting terminal  178 , a haptic module  179 , a camera module  180 , a power management module  188 , a battery  189 , a communication module  190 , a subscriber identification module (SIM)  196 , or an antenna module  197 . In some embodiments, at least one of the components (e.g., the connecting terminal  178 ) may be omitted from the electronic device  101 , or one or more other components may be added in the electronic device  101 . In some embodiments, some of the components (e.g., the sensor module  176 , the camera module  180 , or the antenna module  197 ) may be implemented as a single component (e.g., the display module  160 ). 
     The processor  120  may execute, for example, software (e.g., a program  140 ) to control at least one other component (e.g., a hardware or software component) of the electronic device  101  coupled with the processor  120 , and may perform various data processing or computation. According to one embodiment, as at least part of the data processing or computation, the processor  120  may store a command or data received from another component (e.g., the sensor module  176  or the communication module  190 ) in volatile memory  132 , process the command or the data stored in the volatile memory  132 , and store resulting data in non-volatile memory  134 . According to an embodiment, the processor  120  may include a main processor  121  (e.g., a central processing unit (CPU) or an application processor (AP)), or an auxiliary processor  123  (e.g., a graphics processing unit (GPU), a neural processing unit (NPU), an image signal processor (ISP), a sensor hub processor, or a communication processor (CP)) that is operable independently from, or in conjunction with, the main processor  121 . For example, when the electronic device  101  includes the main processor  121  and the auxiliary processor  123 , the auxiliary processor  123  may be adapted to consume less power than the main processor  121 , or to be specific to a specified function. The auxiliary processor  123  may be implemented as separate from, or as part of the main processor  121 . 
     The auxiliary processor  123  may control, for example, at least some of functions or states related to at least one component (e.g., the display module  160 , the sensor module  176 , or the communication module  190 ) among the components of the electronic device  101 , instead of the main processor  121  while the main processor  121  is in an inactive (e.g., sleep) state, or together with the main processor  121  while the main processor  121  is in an active (e.g., executing an application) state. According to an embodiment, the auxiliary processor  123  (e.g., an image signal processor or a communication processor) may be implemented as part of another component (e.g., the camera module  180  or the communication module  190 ) functionally related to the auxiliary processor  123 . According to an embodiment, the auxiliary processor  123  (e.g., the neural processing unit) may include a hardware structure specified for artificial intelligence module processing. An artificial intelligence module may be generated by machine learning. Such learning may be performed, e.g., by the electronic device  101  where the artificial intelligence module is performed or via a separate server (e.g., the server  108 ). Learning algorithms may include, but are not limited to, e.g., supervised learning, unsupervised learning, semi-supervised learning, or reinforcement learning. The artificial intelligence module may include a plurality of artificial neural network layers. The artificial neural network may be a deep neural network (DNN), a convolutional neural network (CNN), a recurrent neural network (RNN), a restricted Boltzmann machine (RBM), a deep belief network (DBN), a bidirectional recurrent deep neural network (BRDNN), deep Q-network or a combination of two or more thereof but is not limited thereto. The artificial intelligence module may, additionally or alternatively, include a software structure other than the hardware structure. 
     The memory  130  may store various data used by at least one component (e.g., the processor  120  or the sensor module  176 ) of the electronic device  101 . The various data may include, for example, software (e.g., the program  140 ) and input data or output data for a command related thereto. The memory  130  may include the volatile memory  132  or the non-volatile memory  134 . 
     The program  140  may be stored in the memory  130  as software, and may include, for example, an operating system (OS)  142 , middleware  144 , or an application  146 . 
     The input module  150  may receive a command or data to be used by another component (e.g., the processor  120 ) of the electronic device  101 , from the outside (e.g., a user) of the electronic device  101 . The input module  150  may include, for example, a microphone, a mouse, a keyboard, a key (e.g., a button), or a digital pen (e.g., a stylus pen). 
     The sound output module  155  may output sound signals to the outside of the electronic device  101 . The sound output module  155  may include, for example, a speaker or a receiver. The speaker may be used for general purposes, such as playing multimedia or playing record. The receiver may be used for receiving incoming calls. According to an embodiment, the receiver may be implemented as separate from, or as part of the speaker. 
     The display module  160  may visually provide information to the outside (e.g., a user) of the electronic device  101 . The display module  160  may include, for example, a display, a hologram device, or a projector and control circuitry to control a corresponding one of the display, hologram device, and projector. According to an embodiment, the display module  160  may include a touch sensor adapted to detect a touch, or a pressure sensor adapted to measure the intensity of force incurred by the touch. 
     The audio module  170  may convert a sound into an electrical signal and vice versa. According to an embodiment, the audio module  170  may obtain the sound via the input module  150 , or output the sound via the sound output module  155  or an external electronic device (e.g., an electronic device  102  (e.g., a speaker or a headphone)) directly or wirelessly coupled with the electronic device  101 . 
     The sensor module  176  may detect an operational state (e.g., power or temperature) of the electronic device  101  or an environmental state (e.g., a state of a user) external to the electronic device  101 , and then generate an electrical signal or data value corresponding to the detected state. According to an embodiment, the sensor module  176  may include, for example, a gesture sensor, a gyro sensor, an atmospheric pressure sensor, a magnetic sensor, an acceleration sensor, a grip sensor, a proximity sensor, a color sensor, an infrared (IR) sensor, a biometric sensor, a temperature sensor, a humidity sensor, or an illuminance sensor. 
     The interface  177  may support one or more specified protocols to be used for the electronic device  101  to be coupled with the external electronic device (e.g., the electronic device  102 ) directly or wirelessly. According to an embodiment, the interface  177  may include, for example, a high definition multimedia interface (HDMI), a universal serial bus (USB) interface, a secure digital (SD) card interface, or an audio interface. 
     A connecting terminal  178  may include a connector via which the electronic device  101  may be physically connected with the external electronic device (e.g., the electronic device  102 ). According to an embodiment, the connecting terminal  178  may include, for example, an HDMI connector, a USB connector, an SD card connector, or an audio connector (e.g., a headphone connector). 
     The haptic module  179  may convert an electrical signal into a mechanical stimulus (e.g., a vibration or a movement) or electrical stimulus which may be recognized by a user via his tactile sensation or kinesthetic sensation. According to an embodiment, the haptic module  179  may include, for example, a motor, a piezoelectric element, or an electric stimulator. 
     The camera module  180  may capture a still image or moving images. According to an embodiment, the camera module  180  may include one or more lenses, image sensors, image signal processors, or flashes. 
     The power management module  188  may manage power supplied to the electronic device  101 . According to one embodiment, the power management module  188  may be implemented as at least part of, for example, a power management integrated circuit (PMIC). 
     The battery  189  may supply power to at least one component of the electronic device  101 . According to an embodiment, the battery  189  may include, for example, a primary cell which is not rechargeable, a secondary cell which is rechargeable, or a fuel cell. 
     The communication module  190  may support establishing a direct (e.g., wired) communication channel or a wireless communication channel between the electronic device  101  and the external electronic device (e.g., the electronic device  102 , the electronic device  104 , or the server  108 ) and performing communication via the established communication channel The communication module  190  may include one or more communication processors that are operable independently from the processor  120  (e.g., the application processor (AP)) and supports a direct (e.g., wired) communication or a wireless communication. According to an embodiment, the communication module  190  may include a wireless communication module  192  (e.g., a cellular communication module, a short-range wireless communication module, or a global navigation satellite system (GNSS) communication module) or a wired communication module  194  (e.g., a local area network (LAN) communication module or a power line communication (PLC) module). A corresponding one of these communication modules may communicate with the external electronic device  104  via the first network  198  (e.g., a short-range communication network, such as Bluetooth™, wireless-fidelity (Wi-Fi) direct, or infrared data association (IrDA)) or the second network  199  (e.g., a long-range communication network, such as a legacy cellular network, a 5G network, a next-generation communication network, the Internet, or a computer network (e.g., LAN or wide area network (WAN)). These various types of communication modules may be implemented as a single component (e.g., a single chip), or may be implemented as multi components (e.g., multi chips) separate from each other. The wireless communication module  192  may identify or authenticate the electronic device  101  in a communication network, such as the first network  198  or the second network  199 , using subscriber information (e.g., international mobile subscriber identity (IMSI)) stored in the subscriber identification module  196 . 
     The wireless communication module  192  may support a 5G network, after a 4G network, and next-generation communication technology, e.g., new radio (NR) access technology. The NR access technology may support enhanced mobile broadband (eMBB), massive machine type communications (mMTC), or ultra-reliable and low-latency communications (URLLC). The wireless communication module  192  may support a high-frequency band (e.g., the mmWave band) to achieve, e.g., a high data transmission rate. The wireless communication module  192  may support various technologies for securing performance on a high-frequency band, such as, e.g., beamforming, massive multiple-input and multiple-output (massive MIMO), full dimensional MIMO (FD-MIMO), array antenna, analog beam-forming, or large scale antenna. The wireless communication module  192  may support various requirements specified in the electronic device  101 , an external electronic device (e.g., the electronic device  104 ), or a network system (e.g., the second network  199 ). According to an embodiment, the wireless communication module  192  may support a peak data rate (e.g., 20 Gbps or more) for implementing eMBB, loss coverage (e.g., 164 dB or less) for implementing mMTC, or U-plane latency (e.g., 0.5 ms or less for each of downlink (DL) and uplink (UL), or a round trip of 1 ms or less) for implementing URLLC. 
     The antenna module  197  may transmit or receive a signal or power to or from the outside (e.g., the external electronic device) of the electronic device  101 . According to an embodiment, the antenna module  197  may include an antenna including a radiating element composed of a conductive material or a conductive pattern formed in or on a substrate (e.g., a printed circuit board (PCB)). According to an embodiment, the antenna module  197  may include a plurality of antennas (e.g., array antennas). In such a case, at least one antenna appropriate for a communication scheme used in the communication network, such as the first network  198  or the second network  199 , may be selected, for example, by the communication module  190  from the plurality of antennas. The signal or the power may then be transmitted or received between the communication module  190  and the external electronic device via the selected at least one antenna. According to an embodiment, another component (e.g., a radio frequency integrated circuit (RFIC)) other than the radiating element may be additionally formed as part of the antenna module  197 . 
     According to various embodiments, the antenna module  197  may form a mmWave antenna module. According to an embodiment, the mmWave antenna module may include a printed circuit board, an RFIC disposed on a first surface (e.g., the bottom surface) of the printed circuit board, or adjacent to the first surface and capable of supporting a designated high-frequency band (e.g., the mmWave band), and a plurality of antennas (e.g., array antennas) disposed on a second surface (e.g., the top or a side surface) of the printed circuit board, or adjacent to the second surface and capable of transmitting or receiving signals of the designated high-frequency band. 
     At least some of the above-described components may be coupled mutually and communicate signals (e.g., commands or data) therebetween via an inter-peripheral communication scheme (e.g., a bus, general purpose input and output (GPIO), serial peripheral interface (SPI), or mobile industry processor interface (MIPI)). 
     According to an embodiment, commands or data may be transmitted or received between the electronic device  101  and the external electronic device  104  via the server  108  coupled with the second network  199 . Each of the external electronic devices  102  or  104  may be a device of a same type as, or a different type, from the electronic device  101 . According to an embodiment, all or some of operations to be executed at the electronic device  101  may be executed at one or more of the external electronic devices  102 ,  104 , or  108 . For example, if the electronic device  101  should perform a function or a service automatically, or in response to a request from a user or another device, the electronic device  101 , instead of, or in addition to, executing the function or the service, may request the one or more external electronic devices to perform at least part of the function or the service. The one or more external electronic devices receiving the request may perform the at least part of the function or the service requested, or an additional function or an additional service related to the request, and transfer an outcome of the performing to the electronic device  101 . The electronic device  101  may provide the outcome, with or without further processing of the outcome, as at least part of a reply to the request. To that end, a cloud computing, distributed computing, mobile edge computing (MEC), or client-server computing technology may be used, for example. The electronic device  101  may provide ultra low-latency services using, e.g., distributed computing or mobile edge computing. In another embodiment, the external electronic device  104  may include an internet-of-things (IoT) device. The server  108  may be an intelligent server using machine learning and/or a neural network. According to an embodiment, the external electronic device  104  or the server  108  may be included in the second network  199 . The electronic device  101  may be applied to intelligent services (e.g., smart home, smart city, smart car, or healthcare) based on 5G communication technology or IoT-related technology. 
       FIG.  2    is a flowchart illustrating an operation of an electronic device obtaining an image of a user-intended moment according to an embodiment. 
     Referring to  FIG.  2   , according to an embodiment, in operation  210 , the electronic device (e.g., the electronic device  101  of  FIG.  1    or the processor  120  of  FIG.  1   ) may receive information about an image capturing situation from a user. For example, the electronic device may receive the information about the image capturing situation through at least one of a voice input from the user, a drawing input from the user, an image input, and/or the like, including combinations and/or multiples thereof. 
     For example, the electronic device may receive the user&#39;s voice (e.g., “I want to take a picture of a dog with its eyes open and its tongue stuck out in a single-take mode.”) through a microphone (e.g., the input module  150  of  FIG.  1   ) as the information about the image capturing situation. According to an embodiment, the electronic device may receive the user&#39;s drawing (e.g., a picture of a dog&#39;s face with its eyes open and its tongue stuck out) input through a touchscreen (e.g., the display module  160  of  FIG.  1   ) as the information about the image capturing situation. According to an embodiment, the electronic device may receive an input to select one image from among a plurality of images stored in a memory (e.g., the memory  130  of  FIG.  1   ) by the user through the touchscreen as the information about the image capturing situation. 
     According to an embodiment, when at least one of a target sound and a target action is further included in the information about the image capturing situation, the electronic device may store at least one of the target sound and the target action in the memory. According to an embodiment, the target sound may refer to a sound that the user intends to capture. For example, when a user voice input “I want a video of a dog barking” is received as the information about the image capturing situation, a dog barking sound may be the target sound. 
     According to an embodiment, the target action may refer to an action that the user intends to capture. For example, when a user voice input “I want a video of a dog barking” is received as the information about the image capturing situation, a dog barking action may be the target action. 
     According to an embodiment, the electronic device may obtain compressed feature information about a moment when the user intends to capture an image (referred to as an “user-intended moment”), thereby securing the memory and securing efficiency in operation time of the electronic device. Hereinafter, an operation of obtaining compressed feature information will be described. 
     According to an embodiment, in operation  220 , the electronic device may obtain a target image, based on the information about the image capturing situation, using a first artificial intelligence module. According to an embodiment, the first artificial intelligence module may be an artificial intelligence network based on a U-Net or generative adversarial network (GAN). According to an embodiment, the first artificial intelligence module can use, alternatively or additionally, other types of suitable machine learning techniques to obtain the target image. 
     According to an embodiment, the target image may include static feature information that the user wants to capture. For example, the static feature information may be a feature that can be included in one picture, for example, a subject to be shot spreading out the arms or the forehead being seen. 
     According to an embodiment, a voice input of the user, a drawing input of the user, or an image selected by the user may be input data to the first artificial intelligence module, and output data from the first AI module based on the input data may be the target image. For example, the target image may be a sketch image. 
     According to an embodiment, the electronic device may display the target image output from the first artificial intelligence module on the touchscreen. 
     According to an embodiment, when receiving a user manipulation to edit the target image through the touchscreen on which the target image is displayed, the electronic device may edit the target image, based on the user manipulation, and may store the edited target image in the memory. For example, when the user intends an image including a subject&#39;s pose having both arms spreading out but the target image includes a subject&#39;s pose of both arms being slightly bent and open, the user may edit the target image to make both arms spread out through a drawing input through the touchscreen, and the electronic device may store the edited target image in the memory. According to an embodiment, the edited target image may be used to retrain the first artificial intelligence module. 
     According to an embodiment, in operation  230 , the electronic device may obtain feature information included in the target image using a second artificial intelligence module. For example, the feature information may include a low-dimensional feature vector. For example, the low-dimensional feature vector may include a one-dimensional feature vector. 
     According to an embodiment, input data to the second artificial intelligence module may be the target image, and output data from the second artificial intelligence module based on the input data may be the low-dimensional feature vector. For example, the low-dimensional feature vector may be a combination of letters and/or numbers including feature information of an image. 
     According to an embodiment, when the target image is edited by the user, the electronic device may input the edited target image to the second artificial intelligence module, thereby obtaining a low-dimensional feature vector for the edited target image. 
     According to an embodiment, in operation  240 , the electronic device may store the feature information output from the second artificial intelligence module in the memory (e.g., the memory  130  of  FIG.  1   ). 
     According to an embodiment, in operation  250 , based on an image related to the feature information being obtained through a camera (e.g., the camera module  180  of  FIG.  1   ), the electronic device may store the image in the memory. 
     According to an embodiment, the operation of obtaining the image related to the feature information through the camera may include an operation of obtaining a frame related to the feature information among a plurality of frames obtained through the camera or an operation of obtaining an image related to the feature information through a preview of a camera application. 
     According to an embodiment, the electronic device may display the image related to the feature information on the touchscreen. For example, the electronic device may store a plurality of images related to the feature information in a buffer, may display the plurality of images on the touchscreen, and may store at least one image selected by the user in the memory. 
     According to an embodiment, when at least one of the target sound or the target action is stored in the memory and a video related to at least one of the target sound or the target action is obtained through the camera, the electronic device may store the video in the memory. 
     According to an embodiment, the electronic device may classify at least one of sounds or actions included in a plurality of frames obtained through the preview of the camera application, and may obtain a video related to at least one of the target sound or the target action through similarity between at least one of the classified sounds or actions and at least one of the target sound or the target action. 
     According to an embodiment, the electronic device may classify at least one of sounds or actions included in a plurality of frames stored in the memory, and may obtain a video related to at least one of the target sound or the target action through similarity between at least one of the classified sounds or actions and at least one of the target sound or the target action. 
     According to an embodiment, the electronic device may display the video related to at least one of the target sound or the target action on the touchscreen (e.g., the display module  160  of  FIG.  1   ). For example, the electronic device may store a plurality of videos related to at least one of the target sound or the target action in the buffer, may display thumbnails of the plurality of videos on the touchscreen, and may play a video of a thumbnail selected by the user. According to an embodiment, the electronic device may store a video of at least one thumbnail selected by the user among the plurality of thumbnails of the video in the memory. 
       FIG.  3    illustrates an operation of an electronic device storing feature information about a user-intended moment according to an embodiment. 
     According to an embodiment, in operation  310 , the electronic device (e.g., the electronic device  101  of  FIG.  1    or the processor  120  of  FIG.  1   ) may identify that information about an image capturing situation received from a user includes a static situation. For example, the static situation may refer to a pose or form that can be included in one picture, for example, both of a subject&#39;s arms spreading out or the subject&#39;s forehead being seen, and the electronic device may suggest an image including a static situation intended by the user. 
     According to an embodiment, in operation  320 , the electronic device may identify that the information about the image capturing situation received from the user includes a dynamic situation. For example, the dynamic situation may be a situation including a sound or an action, such as jumping, clapping, or dog barking, and the electronic device may suggest a video including a sound or an action intended by the user. 
     By distinguishing between a static situation expressible in a single image and a dynamic situation not expressible in a single image, the user may intuitively describe a situation and may increase accuracy of compression of context information intended by the user. 
     According to an embodiment, in operation  311 , when the information about the image capturing situation received from the user includes the static situation, the electronic device may perform transformation to a 2D image domain. For example, performing the transformation to the 2D domain may refer to obtaining a target image including the information about the image capturing situation. 
     According to an embodiment, the electronic device may obtain the target image using a first artificial intelligence module regardless of whether the information about the image capturing situation received from the user is the user&#39;s voice, the user&#39;s drawing input, or the user&#39;s image selection. For example, the first artificial intelligence module may be an artificial intelligence network based on a U-Net or GAN. 
     According to an embodiment, in operation  312 , the electronic device may edit the target image by a user manipulation. According to an embodiment, when there is an error in the target image output from the first artificial intelligence module or when the target image does not adequately express a situation intended by the user (e.g., when the user intends that a subject have both arms slightly raised but the target image shows that the subject&#39;s arms are raised with the hands slightly bent), the electronic device may edit the target image through manipulation by the user. For example, the user may edit the target image through a drawing input on the target image. According to an embodiment, when the target image does not need editing, operation  312  may be omitted. 
     As described above, since compression in operation  313  is performed after the target image of the 2D domain is obtained regardless of whether the information about the image capturing situation received from the user is the user&#39;s voice, the user&#39;s drawing input, or the user&#39;s image selection, accuracy of a compression operation may be improved. 
     According to an embodiment, in operation  313 , the electronic device may extract feature information included in the target image and may compress the feature information into a low-D vector to determine a degree to which the feature information included in the target image and a frame obtained through a camera (e.g., the camera module  180  of  FIG.  1   ) are matched. For example, the electronic device may extract and compress the feature information using a second artificial intelligence module (e.g., a neural encoder) trained to extract and compress the feature information of the target image. 
     According to an embodiment, the electronic device may input the target image to the second artificial intelligence module, and may compress the feature information of the target image into a low-dimensional (e.g., one-dimensional) feature vector. 
     According to an embodiment, in operation  314 , the electronic device may store the feature information in a memory (e.g., the memory  130  of  FIG.  1   ). For example, the electronic device may store the feature information in a reference database of feature information stored in the memory. 
     As described above, by compressing and storing the feature information of the target image, information loss of data may be minimized and data management may be efficiently performed in terms of capacity or processing speed. 
     According to an embodiment, in operation  321 , when the information about the image capturing situation received from the user includes the dynamic situation, the electronic device may perform transformation to a label. For example, performing the transformation to the label may refer to transforming the information about the image capturing situation to a simple label form, such as walking or a clapping sound, when the information about the image capturing situation includes a target sound or a target action. 
     According to an embodiment, in operation  314 , the electronic device may store the transformed label in the memory. For example, the electronic device may store the label in the reference database of the feature information stored in the memory. 
       FIG.  4    illustrates an operation of an electronic device obtaining a target image of a user-intended moment according to an embodiment. 
     According to an embodiment, referring to  FIG.  4   , when receiving an image  41  as information about an image capturing situation from a user, the electronic device (e.g., the electronic device  101  of  FIG.  1    or the processor  120  of  FIG.  1   ) may obtain a target image  42  through a first artificial intelligence module  410 . For example, the first artificial intelligence module  410  may be a U-Net or GAN-based artificial intelligence network, which may be trained to transform an image into a sketch image. According to an embodiment, when the user selects an image including a pose or form desired to be captured from among a plurality of images through a touchscreen (e.g., the display module  160  of  FIG.  1   ), the electronic device may input the selected image to the first artificial intelligence module  410  and may obtain the target image  42  as output data. 
     According to an embodiment, when receiving a drawing input (e.g., a sketch)  43  as information about an image capturing situation from the user, the electronic device may obtain a target image  44  through a first artificial intelligence module  411 . For example, the first artificial intelligence module  411  may be a U-Net or GAN-based artificial intelligence network, which may be trained to transform a drawing image into a sketch image. According to an embodiment, when the user inputs a drawing including a pose or form desired to be captured through a touchscreen (e.g., the display module  160  of  FIG.  1   ), the electronic device may input a drawing image to the first artificial intelligence module  411  and may obtain the target image  44  as output data. 
     According to an embodiment, when receiving a sound  45  (e.g., a voice) as information about an image capturing situation from the user, the electronic device may transform the sound  45  into text through a sound-to-text module  46 . According to an embodiment, the sound-to-text module  46  includes a neural network trained convert sound to text, such as a speech-to-text deep learning neural network. According to an embodiment, the sound-to-text module  46  can implement natural language processing techniques. 
     According to an embodiment, the electronic device may obtain a target image  47  by inputting the transformed text from the sound to text module  46  into a first artificial intelligence module  412 . For example, the first artificial intelligence module  412  may be a U-Net or GAN-based artificial intelligence network, which may be trained to transform text into a sketch image. According to an embodiment, when the user inputs a sound including a pose or form desired to be captured through a microphone (e.g., the input module  150  of  FIG.  1   ), the electronic device may transform the sound into text, may input the text to the first artificial intelligence module  412  and may obtain the target image  47  as output data. 
       FIG.  5 A  illustrates an operation of training an artificial intelligence module (e.g., a first artificial intelligence module) to obtain a target image at a user-intended moment of an electronic device according to an embodiment. 
     According to an embodiment, referring to  FIG.  5 A , the electronic device (e.g., the electronic device  101  of  FIG.  1    or the processor  120  of  FIG.  1   ) may train a U-Net or GAN-based first artificial intelligence module  510  that transforms a general photo  51  (e.g., “reference”) into a sketch image  52  (e.g., “target”). According to an embodiment, the general photo  51  and the sketch image  52  represent training data used to train the first artificial intelligence module  510 . 
     According to an embodiment, after training the first artificial intelligence module  510  with a suitable amount of training data or greater (e.g., enough training data to return satisfactory results (e.g., results with a probability of greater than a threshold amount)), the electronic device may implement the first artificial intelligence module  510  to perform inference. 
       FIG.  5 B  illustrates an operation of training an artificial intelligence module (e.g., a second artificial intelligence module) to obtain feature information about a user-intended moment of an electronic device according to an embodiment. 
     According to an embodiment, referring to  FIG.  5 B , the electronic device (e.g., the electronic device  101  of  FIG.  1    or the processor  120  of  FIG.  1   ) may train a second artificial intelligence module  530 , based on triplet loss. 
     For example, the electronic device may input as training data a reference image  51 , a negative image  51 - 1 , and a positive image  51 - 2  which are prepared to trained first artificial intelligence modules  520 ,  521 , and  522 , respectively. For example, the negative image  51 - 1  may be an image of which a similarity to the reference image  51  is less than a configured value (e.g., a threshold), and the positive image  51 - 2  may be an image of which a similarity to the reference image  510  is equal to or greater than the configured value (e.g., the threshold). For example, the trained first artificial intelligence modules  520 ,  521 , and  522  may be trained to transform an image into a sketch as shown in  FIG.  5 A . 
     According to an embodiment, target images of the reference image  51 , the negative image  51 - 1 , and the positive image  51 - 2  may be output respectively from the trained first artificial intelligence modules  520 ,  521 , and  522 , and each output target image may be input to a second artificial intelligence module  530 . According to an embodiment, the second artificial intelligence module  530  may be a neural encoder for extracting and compressing feature information of a target image and/or other suitable machine learning technique. 
     According to an embodiment, the electronic device may obtain a triplet loss  54 , which may be based on low-dimensional feature vectors  53 ,  53 - 1 , and  53 - 2  of the respective target images output from the second artificial intelligence module  530 , and may train the second artificial intelligence module  530  based on the obtained triplet loss  54 . For example, the triplet loss  54  may be for training the second artificial intelligence model  530  such that the low-dimensional feature vector  53  for the reference image  51  and the low-dimensional feature vector  53 - 1  for the negative image  15 - 1  become distant from each other, and the low-dimensional feature vector  53  for the reference image  51  and the low-dimensional feature vector  53 - 2  for the positive image  15 - 2  become close to each other. That is, the second artificial intelligence model  530  is trained to generate the low-dimensional feature vector  53 - 2  to be similar to the low-dimensional feature vector  53  for the reference image  51 . 
     According to an embodiment, after training the second artificial intelligence module  530  with a suitable amount of data or greater (e.g., enough training data to return satisfactory results (e.g., results with a probability of greater than a threshold amount)), the electronic device may implement the second artificial intelligence module  530  to perform inference. 
       FIG.  5 C  illustrates an operation of training an artificial intelligence module to obtain feature information about a user-intended moment of an electronic device, according to an embodiment. 
     According to an embodiment, referring to  FIG.  5 C , the electronic device (e.g., the electronic device  101  of  FIG.  1    or the processor  120  of  FIG.  1   ) may train a first artificial intelligence module  510  using a sound, a drawing, or an image including similar features as input data. 
     According to an embodiment, the electronic device may input a reference image  51  to the first artificial intelligence module  510 , and may input a target image output from the first artificial intelligence module  510  to a trained second artificial intelligence module  550 . For example, the first artificial intelligence module  510  to which the reference image  51  is input may be an artificial intelligence network for transforming an image into a sketch. According to an embodiment, the trained second artificial intelligence module  550  may be trained as shown in  FIG.  5 B . 
     According to an embodiment, the electronic device may obtain, as a reference low-dimensional feature vector  58 , a low-dimensional feature vector output from the trained second artificial intelligence module  550  using the reference image  51  as input data. 
     According to an embodiment, the electronic device may input a plurality of drawing images  56  to a first artificial intelligence module  511 , and may input a target image output from the first artificial intelligence module  511  to a trained second artificial intelligence module  550 . For example, the trained first artificial intelligence module  511  to which the drawing images  56  are input may be an artificial intelligence network for transforming a drawing image into a sketch. According to an embodiment, the trained second artificial intelligence module  550  may be trained as shown in  FIG.  5 B . 
     According to an embodiment, the electronic device may compare a low-dimensional feature vector  58 - 1  output from the trained second artificial intelligence module  550  using the drawing images  56  as input data with the reference low-dimensional feature vector  58 , thereby obtaining a loss  59 . According to an embodiment, the electronic device may train the trained first artificial intelligence module  511  for transforming the drawing images into a sketch, based on the loss  59 . 
     According to an embodiment, the electronic device may input a user voice  55  to a voice-to-text module  540 , thereby obtaining text  57  (e.g., “Raise both arms up to sky with elbows bent”) corresponding to the user voice  55 . 
     According to an embodiment, the electronic device may input the text  57  to a trained first artificial intelligence module  512 , and may input a target image output from the first artificial intelligence module  512  to a trained second artificial intelligence module  550 . For example, the first artificial intelligence module  512  to which the text  57  is input may be an artificial intelligence network for transforming text into a sketch. According to an embodiment, the trained second artificial intelligence module  550  may be trained as shown in  FIG.  5 B . 
     According to an embodiment, the electronic device may compare a low-dimensional feature vector  58 - 1  output from the trained second artificial intelligence module  550  using the text  57  as input data with the reference low-dimensional feature vector  58 , thereby obtaining a loss  59 . According to an embodiment, the electronic device may train the trained first artificial intelligence module  512  for transforming the text images into a sketch, based on the loss  59 . 
       FIG.  6    illustrates an operation of obtaining feature information about a user-intended moment, based on the user&#39;s voice, according to an embodiment. 
     According to an embodiment, referring to  FIG.  6   , an electronic device (e.g., the electronic device  101  or the processor  120  of  FIG.  1   ) may receive a user voice  610  saying “Hi, Bixby. I want a dog with tongue out and a barking dog would be cute” as information about an image capturing situation from a user  10 . 
     According to an embodiment, the electronic device may transform the user voice  610  into text, and may obtain “a dog with tongue out”  620  as text related to a static situation included in the text and “a barking dog” as text related to a dynamic situation. 
     According to an embodiment, the electronic device may input “a dog with tongue out”  620  as the text related to the static situation to a first artificial intelligence module (e.g., the trained first artificial intelligence module  512 ), thereby obtaining a target image  630 . For example, the first artificial intelligence module may be trained as shown in  FIGS.  5 A and/or  5 C . For example, the target image  630  may be a 2D image, which may be a sketch image including a dog sticking out its tongue. 
     According to an embodiment, the electronic device may input the target image  630  to a second artificial intelligence module (e.g., the trained second artificial intelligence module  550 ), thereby obtaining a low-dimensional (e.g., one-dimensional) feature vector  640 . For example, the second artificial intelligence module may be trained as shown in  FIG.  5 B . 
     According to an embodiment, the electronic device may store the low-dimensional feature vector  640  in a reference database  650 . For example, the electronic device may store the low-dimensional feature vector  640  as an item “dog with tongue”  651  in the reference database  650 . 
     According to an embodiment, the electronic device may obtain a label “dog barking”  660  related to a target sound, based on “a barking dog” as the text related to the dynamic situation. 
     According to an embodiment, the electronic device may store the label “dog barking”  660  as an item “dog barking”  652  in the reference database  650 . 
       FIG.  7    illustrates an operation of obtaining an image or a video at a user-intended moment in a single-take mode according to an embodiment. 
     According to an embodiment, referring to  FIG.  7   , in operation  710 , an electronic device (e.g., the electronic device  101  of  FIG.  1    or the processor  120  of  FIG.  1   ) may capture a video in a single-take mode. For example, the single-take mode may refer to a function of collecting a plurality of frames for a predetermined time specified by a user and extracting a plurality of images or videos according to a configured criterion. 
     According to an embodiment, in operation  720 , the electronic device may compress the obtained frames captured in operation  710  into low-dimensional vectors. 
     According to an embodiment, in operation  730 , the electronic device may compare feature information for each of the obtained frames. For example, the electronic device may identify whether feature information is included in each acquired frame using an action classifier  731  and/or a sound classifier  732 . 
     According to an embodiment, the feature information may be obtained from a pre-built reference database in operation  734 , based on information about an image capturing situation input from the user in real time in operation  733 . For example, the reference database may be the reference database established by operation  314  of  FIG.  3    and/or the reference database  650  of  FIG.  6   . 
     According to an embodiment, in operation  740 , the electronic device may suggest an image and/or a video according to predefined metrics. For example, the electronic device may extract at least one frame including a feature stored in the reference database among the plurality of frames collected for a predetermined time and may store the frame or may suggest the frame to the user. 
     Accordingly, an image obtaining operation according to the disclosure has an effect of adding a new feature defined by a user without affecting a pipeline of an existing single-take mode. 
       FIG.  8    illustrates an operation of obtaining an image at a user-intended moment in a single-take mode according to an embodiment. 
     According to an embodiment, referring to  FIG.  8   , an electronic device (e.g., the electronic device  101  of  FIG.  1    or the processor  120  of  FIG.  1   ) may execute a camera application by a user manipulation, and may obtain a plurality of frames  820  for a period designated by the user through a single-take mode  810 . For example, the period designated by the user may be substantially five seconds (or another suitable period of time) after the user selects a shutter button for image capturing. 
     According to an embodiment, the electronic device may provide the user with an image satisfying a configured condition among the plurality of frames  820  through a single-take suggestion pipeline  830 . For example, when the configured condition is such that a frame has no blur and has good color and composition, the electronic device may suggest a frame  840  satisfying the condition among the plurality of frames  820 . 
     According to an embodiment, when information about a user-intended moment is stored by the user, the electronic device may further suggest a frame including information of the user-intended moment to the user. 
     According to an embodiment, when the user inputs a reference image  81 , the electronic device may store a pose  1  vector  82  which is feature information of the reference image  81 , based on the reference image  81 . For example, the electronic device may obtain and store the pose  1  vector  82  which is the feature information of the reference image  81  using the first artificial intelligence module  510  shown in  FIG.  5 A  and the second artificial intelligence module  530  shown in  FIG.  5 B . 
     According to an embodiment, the electronic device may transform a feature of each of the plurality of frames  820  into a low-dimensional feature vector, and may compare, at block  821 , the low-dimensional feature vector of each of the plurality of frames  820  with pieces of feature information stored in a reference database. 
     According to an embodiment, the electronic device may further suggest a frame  841  including feature information stored in the reference database to the user through the single-take suggestion pipeline  830 . For example, the electronic device may further suggest the frame  841  including a pose of raising both arms which is a feature of the reference image  81  input by the user to the user. 
       FIG.  9    illustrates an operation of obtaining an image and a video at a user-intended moment in a single-take mode according to an embodiment. 
     According to an embodiment, referring to  FIG.  9   , an electronic device (e.g., the electronic device  101  of  FIG.  1    or the processor  120  of  FIG.  1   ) may execute a camera application by a user manipulation, and may obtain a plurality of frames  920  for a period designated by the user through a single-take mode  910 . For example, the period designated by the user may be substantially five seconds (or another suitable period of time) after the user selects a shutter button for image capturing. 
     According to an embodiment, the electronic device may provide the user with an image satisfying a configured condition among the plurality of frames  920  through a single-take suggestion pipeline  930 . For example, when the configured condition is such that a frame has no blur and has good color and composition, the electronic device may suggest a frame  940  satisfying the condition among the plurality of frames  920 . 
     According to an embodiment, when information about a user-intended moment is stored by the user, the electronic device may further suggest a frame including information of the user-intended moment to the user. 
     According to an embodiment, when the user inputs a drawing (sketch  91 ) or a user voice  92  (e.g., “Hi, Bixby. I want a barking dog”), the electronic device may store feature information ‘dog with tongue vector’  93  or ‘dog barking label’  94 , based on the sketch  91  or the user voice  92 . For example, the electronic device may obtain and store the feature information ‘dog with tongue vector’  93  of the drawing image  91  using the first artificial intelligence module  510  shown in  FIG.  5 A  and the second artificial intelligence module  530  shown in  FIG.  5 B . 
     According to an embodiment, the electronic device may transform a feature of each of the plurality of frames  920  into a low-dimensional feature vector, and may compare, at block  921 , the low-dimensional feature vector of each of the plurality of frames  920  with pieces of feature information stored in a reference database. 
     According to an embodiment, the electronic device may further suggest a frame  941  or a video  942  including feature information stored in the reference database to the user through the single-take suggestion pipeline  930 . For example, the electronic device may further suggest, to the user, an image  941  of a dog sticking out its tongue input by a user via the sketch  91  and a video  942  of a dog barking input by the user via the user voice  92 . 
       FIG.  10    illustrates an operation of obtaining an image and a video at a user-intended moment in a single-take mode according to an embodiment. 
     According to an embodiment, referring to  FIG.  10   , an electronic device (e.g., the electronic device  101  of  FIG.  1    or the processor  120  of  FIG.  1   ) may store the frames for substantially five seconds (or another suitable period of time) after a shutter button is selected in a buffer through a single-take mode of a camera application. For example, a plurality of frames obtained for five seconds after the shutter button is selected may sequentially include a dog sitting, a dog sticking out its tongue, a dog lying down, a dog yawning, a dog barking, and a dog sticking out its tongue as shown. 
     According to an embodiment, the electronic device may obtain images  1020  and  1021  of a dog sticking out its tongue and a video  1030  of a dog barking, based on “dog with tongue vector” and “dog barking vector” stored in a reference database, and may store the images  1020  and  1021  and the video  1030  in a memory (e.g., the memory  130  of  FIG.  1   ) or may provide the same to a user through a touchscreen (e.g., the display module  160  of  FIG.  1   ) or other suitable display. 
       FIG.  11    illustrates an operation of obtaining an image and a video at a user-intended moment through a preview of a camera application according to an embodiment. 
     According to an embodiment, referring to  FIG.  11   , an electronic device (e.g., the electronic device  101  of  FIG.  1    or the processor  120  of  FIG.  1   ) may display an execution screen of a camera application by a user manipulation, and a user may select a reference image  1110  through the execution screen of the camera application. 
     According to an embodiment, when the user inputs the reference image  1110 , the electronic device may obtain or store a pose  1  vector  1111  which is feature information of the reference image  1110 , based on the reference image  1110 . For example, the electronic device may obtain and store the pose  1  vector  1111  which is the feature information of the reference image  1110  using the first artificial intelligence module  510  shown in  FIG.  5 A  and the second artificial intelligence module  530  shown in  FIG.  5 B . 
     According to an embodiment, when a frame including the pose  1  vector  1111  stored by the user input is identified in a preview  1120  of the camera application, the electronic device may automatically operate a shutter button for obtaining an image, or may automatically operate a shutter button for obtaining a video. 
     According to an embodiment, in a case of operating in an image mode, when identifying a frame  1131  including the pose  1  vector  111  while identifying frames  1130 ,  1131 , and  1132  respectively including a plurality of poses of the user through the preview  1120 , the electronic device may automatically operate the shutter button to store the frame  1131  including the pose  1  vector  1111 . 
     According to an embodiment, in a case of operating in a video mode, when identifying a frame including the pose  1  vector  1111  through the preview  1120 , the electronic device may automatically operate the shutter button to take a video  1140  starting from the frame including the pose  1  vector  1111  for a configured time. 
       FIG.  12    illustrates an operation of obtaining an image and a video at a user-intended moment through a preview of a camera application according to an embodiment. 
     According to an embodiment, referring to  FIG.  12   , an electronic device (e.g., the electronic device  101  of  FIG.  1    or the processor  120  of  FIG.  1   ) may display an execution screen of a camera application by a user manipulation, and a user may obtain a drawing image  1210  by the user drawing through the execution screen of the camera application. 
     According to an embodiment, when the drawing image  1210  is obtained, the electronic device may obtain or store a ‘tongue-sticking-out dog vector’  1211  which is feature information of the drawing image  1210 , based on the drawing image  1210 . For example, the electronic device may obtain and store the ‘tongue-sticking-out dog vector’  1211  which is the feature information of the drawing image  1210  using the first artificial intelligence module  510  shown in  FIG.  5 A  and the second artificial intelligence module  530  shown in  FIG.  5 B . 
     According to an embodiment, when a frame including the ‘tongue-sticking-out dog vector’  1211  stored by a user input is identified in a preview  1220  of the camera application, the electronic device may automatically operate a shutter button for obtaining an image, or may automatically operate a shutter button for obtaining a video. 
     According to an embodiment, in a case of operating in an image mode, when identifying frames  1231  and  1232  including the ‘tongue-sticking-out dog vector’  1211  while identifying frames  1230 ,  1231 ,  1232 , and  1233  respectively including a plurality of poses of a dog through the preview  1220 , the electronic device may automatically operate the shutter button to store the frames  1231  and  1232  including the ‘tongue-sticking-out dog vector’  1211 . 
     According to an embodiment, in a case of operating in a video mode, when identifying a frame including the ‘tongue-sticking-out dog vector’  1211  through the preview  1220 , the electronic device may automatically operate the shutter button to take a video  1240  starting from the frame including the ‘tongue-sticking-out dog vector’  1211  for a configured time. 
     According to an embodiment, an electronic device (e.g., the electronic device  101  of  FIG.  1   ) may include a camera (e.g., the camera module  180  of  FIG.  1   ), a memory (e.g., the memory  130  of  FIG.  1   ), and a processor (e.g., the processor  120  of  FIG.  1   ), wherein the processor may receive information about an image capturing situation from a user, may obtain a target image based on the information about the image capturing situation using a first artificial intelligence module stored in the memory, may obtain feature information included in the target image using a second artificial intelligence module stored in the memory, may store the feature information in the memory, and may store an image related to the feature information in the memory when obtaining the image related to the feature information through the camera. 
     According to an embodiment, the processor may display the target image on a touchscreen (e.g., the display module  160  of  FIG.  1   ). 
     According to an embodiment, the processor may edit the target image based on a user manipulation for editing the target image displayed on the touchscreen when receiving the user manipulation, and may obtain feature information of the edited target image using the second artificial intelligence module. 
     According to an embodiment, the processor may receive the information about the image capturing situation through at least one of a voice input of the user, a drawing input of the user, or an image input. 
     According to an embodiment, the processor may store at least one of a target sound or a target action in the memory when the information about the image capturing situation further includes at least one of the target sound or the target action, and may store a video related to at least one of the target sound or the target action in the memory when obtaining the video through the camera. 
     According to an embodiment, the processor may classify at least one of a sound or an action included in a plurality of frames obtained through a preview of a camera application, and may obtain the video through similarity between at least one of the classified sound or action and the of the target sound or the target action. 
     According to an embodiment, the processor may classify at least one of a sound or an action included in a plurality of frames stored in the memory, and may obtain the video through similarity between at least one of the classified sound or action and the at least one of the target sound or the target action. 
     According to an embodiment, the processor may obtain the image related to the feature information among a plurality of frames obtained through a preview of a camera application. 
     According to an embodiment, the processor may obtain the image related to the feature information among a plurality of frames stored in the memory. 
     According to an embodiment, the feature information may include a one-dimensional feature vector. 
     According to an embodiment, a method for controlling an electronic device may include receiving information about an image capturing situation from a user, obtaining a target image based on the information about the image capturing situation using a first artificial intelligence module stored in a memory, obtaining feature information included in the target image using a second artificial intelligence module stored in the memory, storing the feature information in the memory, and storing an image related to the feature information in the memory when obtaining the image related to the feature information through a camera. 
     According to an embodiment, the method may further include displaying the target image on a touchscreen and editing the target image based on a user manipulation for editing the target image displayed on the touchscreen when receiving the user manipulation, wherein the obtaining of the feature information may include obtaining feature information of the edited target image using the second artificial intelligence module. 
     According to an embodiment, the receiving may include receiving the information about the image capturing situation through at least one of a voice input of the user, a drawing input of the user, or an image input. 
     According to an embodiment, the method may further include storing at least one of a target sound or a target action in the memory when the information about the image capturing situation further includes at least one of the target sound or the target action, and storing a video related to at least one of the target sound or the target action in the memory when obtaining the video through the camera. 
     According to an embodiment, the storing of the video in the memory may include classifying at least one of a sound or an action included in a plurality of frames obtained through a preview of a camera application, and obtaining the video through similarity between at least one of the classified sound or action and the at least one of the target sound or the target action. 
     According to an embodiment, the storing of the video in the memory may include classifying at least one of a sound or an action included in a plurality of frames stored in the memory, and obtaining the video through similarity between at least one of the classified sound or action and the at least one of the target sound or the target action. 
     According to an embodiment, the storing the image in the memory may include obtaining the image related to the feature information among a plurality of frames obtained through a preview of a camera application. 
     According to an embodiment, the storing the image in the memory may include obtaining the image related to the feature information among a plurality of frames stored in the memory. 
     According to an embodiment, the feature information may include a one-dimensional feature vector. 
     According to an embodiment, a method may include: capturing a video in a single-take mode, the single take mode collecting a plurality of frames for a predetermined time, compressing the plurality of frames into low-dimensional vectors, comparing feature information for each of the plurality of frames, wherein the feature information is obtained from a reference database based on information about an image capturing situation input from a user, extracting at least one frame including a feature stored in the reference database among the plurality of frames collected for the predetermined time, and storing the extracted at least one frame or suggesting the extracted at least one frame to the user. 
     The electronic device according to various embodiments may be one of various types of electronic devices. The electronic devices may include, for example, a portable communication device (e.g., a smartphone), a computer device, a portable multimedia device, a portable medical device, a camera, a wearable device, or a home appliance. According to an embodiment of the disclosure, the electronic devices are not limited to those described above. 
     It should be appreciated that various embodiments of the disclosure and the terms used therein are not intended to limit the technological features set forth herein to particular embodiments and include various changes, equivalents, or replacements for a corresponding embodiment. With regard to the description of the drawings, similar reference numerals may be used to refer to similar or related elements. It is to be understood that a singular form of a noun corresponding to an item may include one or more of the things, unless the relevant context clearly indicates otherwise. As used herein, each of such phrases as “A or B”, “at least one of A and B”, “at least one of A or B”, “A, B, or C”, “at least one of A, B, and C”, and “at least one of A, B, or C”, may include any one of, or all possible combinations of the items enumerated together in a corresponding one of the phrases. As used herein, such terms as “ 1 st” and “ 2 nd”, or “first” and “second” may be used to simply distinguish a corresponding component from another, and does not limit the components in other aspect (e.g., importance or order). It is to be understood that if an element (e.g., a first element) is referred to, with or without the term “operatively” or “communicatively”, as “coupled with”, “coupled to”, “connected with”, or “connected to” another element (e.g., a second element), it means that the element may be coupled with the other element directly (e.g., wiredly), wirelessly, or via a third element. 
     As used in connection with various embodiments of the disclosure, the term “module” may include a unit implemented in hardware, software, or firmware, and may interchangeably be used with other terms, for example, “logic”, “logic block”, “part”, or “circuitry”. A module may be a single integral component, or a minimum unit or part thereof, adapted to perform one or more functions. For example, according to an embodiment, the module may be implemented in a form of an application-specific integrated circuit (ASIC). 
     Various embodiments as set forth herein may be implemented as software (e.g., the program  140 ) including one or more instructions that are stored in a storage medium (e.g., internal memory  136  or external memory  138 ) that is readable by a machine (e.g., the electronic device  101 ). For example, a processor (e.g., the processor  120 ) of the machine (e.g., the electronic device  101 ) may invoke at least one of the one or more instructions stored in the storage medium, and execute it. This allows the machine to be operated to perform at least one function according to the at least one instruction invoked. The one or more instructions may include a code generated by a complier or a code executable by an interpreter. The machine-readable storage medium may be provided in the form of a non-transitory storage medium. Wherein, the term “non-transitory” simply means that the storage medium is a tangible device, and does not include a signal (e.g., an electromagnetic wave), but this term does not differentiate between where data is semi-permanently stored in the storage medium and where the data is temporarily stored in the storage medium. 
     According to an embodiment, a method according to various embodiments of the disclosure may be included and provided in a computer program product. The computer program product may be traded as a product between a seller and a buyer. The computer program product may be distributed in the form of a machine-readable storage medium (e.g., compact disc read only memory (CD-ROM)), or be distributed (e.g., downloaded or uploaded) online via an application store (e.g., PlayStore™), or between two user devices (e.g., smart phones) directly. If distributed online, at least part of the computer program product may be temporarily generated or at least temporarily stored in the machine-readable storage medium, such as memory of the manufacturer&#39;s server, a server of the application store, or a relay server. 
     According to various embodiments, each component (e.g., a module or a program) of the above-described components may include a single entity or multiple entities, and some of the multiple entities may be separately disposed in different components. According to various embodiments, one or more of the above-described components or operations may be omitted, or one or more other components or operations may be added. Alternatively or additionally, a plurality of components (e.g., modules or programs) may be integrated into a single component. In such a case, the integrated component may still perform one or more functions of each of the plurality of components in the same or similar manner as they are performed by a corresponding one of the plurality of components before the integration. According to various embodiments, operations performed by the module, the program, or another component may be carried out sequentially, in parallel, repeatedly, or heuristically, or one or more of the operations may be executed in a different order or omitted, or one or more other operations may be added.