CN115481271A - Photo classification method and device - Google Patents

Photo classification method and device Download PDF

Info

Publication number
CN115481271A
CN115481271A CN202110586187.8A CN202110586187A CN115481271A CN 115481271 A CN115481271 A CN 115481271A CN 202110586187 A CN202110586187 A CN 202110586187A CN 115481271 A CN115481271 A CN 115481271A
Authority
CN
China
Prior art keywords
photo
album
stored
type
information
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
CN202110586187.8A
Other languages
Chinese (zh)
Inventor
马强
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Beijing Xiaomi Mobile Software Co Ltd
Original Assignee
Beijing Xiaomi Mobile Software Co Ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Beijing Xiaomi Mobile Software Co Ltd filed Critical Beijing Xiaomi Mobile Software Co Ltd
Priority to CN202110586187.8A priority Critical patent/CN115481271A/en
Publication of CN115481271A publication Critical patent/CN115481271A/en
Pending legal-status Critical Current

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/50—Information retrieval; Database structures therefor; File system structures therefor of still image data
    • G06F16/55—Clustering; Classification
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/50—Information retrieval; Database structures therefor; File system structures therefor of still image data
    • G06F16/58—Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually
    • G06F16/583—Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually using metadata automatically derived from the content
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/50—Information retrieval; Database structures therefor; File system structures therefor of still image data
    • G06F16/58—Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually
    • G06F16/583—Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually using metadata automatically derived from the content
    • G06F16/5838—Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually using metadata automatically derived from the content using colour
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods

Landscapes

  • Engineering & Computer Science (AREA)
  • Theoretical Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Library & Information Science (AREA)
  • Data Mining & Analysis (AREA)
  • General Engineering & Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Databases & Information Systems (AREA)
  • Artificial Intelligence (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Health & Medical Sciences (AREA)
  • Biomedical Technology (AREA)
  • Biophysics (AREA)
  • Computational Linguistics (AREA)
  • Evolutionary Computation (AREA)
  • General Health & Medical Sciences (AREA)
  • Molecular Biology (AREA)
  • Computing Systems (AREA)
  • Mathematical Physics (AREA)
  • Software Systems (AREA)
  • Processing Or Creating Images (AREA)

Abstract

The disclosure provides a photo classification method and device. The method is applied to the electronic equipment and comprises the following steps: and when a new photo to be stored is detected, acquiring the characteristic information of the photo to be stored, determining the photo type of the photo to be stored according to the characteristic information, and storing the photo to be stored into a target album corresponding to the photo type. The electronic equipment classifies the photos according to the characteristic information of the photos to be stored and the albums for storing different photo types, so that the photo classification mode of the electronic equipment is enriched. When the electronic equipment comprises a plurality of albums for storing photos of different photo types, the electronic equipment can classify the photos of multiple types by executing the method provided by the embodiment, and the requirement of a user on classifying the photos of multiple types is met.

Description

Photo classification method and device
Technical Field
The present disclosure relates to the field of computer communication technologies, and in particular, to a method and an apparatus for classifying photos.
Background
Some electronic devices upload the photos to be stored to the server after acquiring the photos to be stored, acquire the photo types of the photos to be stored issued by the server, label the photo types for the photos to be stored, and store the photos labeled with the photo types in a local album. After receiving a request for viewing the photos of the specified type, the electronic equipment acquires the photos of the required type from the local album by identifying the photo type marked by the photos in the local album.
In the method, the electronic equipment can only classify the photos according to the types of the photos issued by the server. How to enrich the way of classifying photos by electronic devices is a technical problem to be urgently solved by technical personnel in the field.
Disclosure of Invention
To overcome the problems in the related art, the present disclosure provides a photo classification method and apparatus.
According to a first aspect of the embodiments of the present disclosure, there is provided a photo classification method applied to an electronic device, the method including:
when a new photo to be stored is detected, acquiring the characteristic information of the photo to be stored, and determining the photo type of the photo to be stored according to the characteristic information;
searching whether a target album corresponding to the photo type exists or not according to the photo type, wherein the target album is a system default album or a custom album pre-created by a user;
and if the target photo album exists, storing the photo to be stored into the target photo album.
Optionally, the method further comprises:
and after receiving a creation instruction for creating the photo album, generating a corresponding custom photo album according to the creation instruction.
Optionally, the obtaining the feature information of the photo to be stored, and determining the photo type of the photo to be stored according to the feature information includes:
inputting the photo to be stored into a type recognition neural network, enabling the type recognition neural network to acquire the characteristic information of the photo to be stored, determining the type of the photo according to the characteristic information, and outputting the type of the photo.
Optionally, the generating of the type recognition neural network includes:
responding to an operation of storing a sample photo in the custom photo album, and acquiring marking information set for the sample photo, wherein the marking information comprises preset type information of the custom photo album;
inputting the sample photo into a recognition network to be trained so that the recognition network to be trained outputs actual type information obtained by recognition of the sample photo;
and adjusting parameters in the recognition network to be trained based on the difference between the preset type information and the actual type information to obtain the type recognition neural network.
Optionally, the method further comprises:
and responding to the condition of meeting network adjustment, and adjusting parameters in the type recognition neural network according to the current stored photos in the custom album and the preset type information.
Optionally, the method further comprises:
and after the target photo album is determined, outputting inquiry information, wherein the inquiry information is used for inquiring whether the photo to be stored is stored in the target photo album, and responding to a received instruction for storing the photo to be stored in the target photo album, and storing the photo to be stored in the target photo album.
Optionally, the method further comprises:
and responding to the absence of the target album, and outputting prompt information.
According to a second aspect of the embodiments of the present disclosure, there is provided a photo classification apparatus applied to an electronic device, the apparatus including:
the photo type determining module is configured to acquire the feature information of the photo to be stored after a new photo to be stored is detected, and determine the photo type of the photo to be stored according to the feature information;
the target album searching module is configured to search whether a target album corresponding to the photo type exists or not according to the photo type, wherein the target album is a system default album or a custom album created by a user in advance;
and the photo storage module is configured to store the photo to be stored into the target photo album if the target photo album exists.
Optionally, the apparatus further comprises:
and the album generating module is configured to generate a corresponding custom album according to the creation instruction after receiving the creation instruction for newly creating the album.
Optionally, the photo type determining module is configured to input the photo to be stored into a type recognition neural network, so that the type recognition neural network obtains the feature information of the photo to be stored, determines the type of the photo according to the feature information, and outputs the type of the photo.
Optionally, the apparatus further comprises:
the annotation information acquisition module is configured to respond to an operation of storing a sample photo in the custom album, and acquire annotation information set for the sample photo, wherein the annotation information comprises preset type information of the custom album;
the sample photo input module is configured to input the sample photo into a recognition network to be trained so that the recognition network to be trained outputs actual type information obtained by recognition of the sample photo;
a first parameter adjusting module configured to adjust parameters in the recognition network to be trained based on a difference between the preset type information and the actual type information to obtain the type recognition neural network.
Optionally, the apparatus further comprises:
and the second parameter adjusting module is configured to adjust the parameters in the type recognition neural network according to the pictures currently stored in the custom album and the preset type information in response to the network adjusting condition being met.
Optionally, the apparatus further comprises:
the query information output module is configured to output query information after the target photo album is determined, wherein the query information is used for querying whether the photo to be stored is stored in the target photo album;
the photo storage module is configured to store the photo to be stored into the target album in response to receiving an instruction for storing the photo to be stored into the target album.
Optionally, the apparatus further comprises:
a prompt information output module configured to output a prompt information in response to the target album not being present.
According to a third aspect of embodiments of the present disclosure, there is provided a non-transitory computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the method of any one of the above first aspects.
According to a fourth aspect of an embodiment of the present disclosure, there is provided an electronic apparatus including:
a processor;
a memory for storing processor-executable instructions;
wherein the processor is configured to execute the instructions to implement the method of any of the first aspect above.
The technical scheme provided by the embodiment of the disclosure can have the following beneficial effects:
the embodiment of the disclosure provides a photo classification method, wherein after detecting a new photo to be stored, an electronic device acquires feature information of the photo to be stored, determines a photo type of the photo to be stored according to the feature information, and stores the photo to be stored in a target album corresponding to the photo type. The electronic equipment classifies the photos according to the characteristic information of the photos to be stored and the photo albums for storing different photo types, so that the photo classifying mode of the electronic equipment is enriched.
When the electronic equipment comprises a plurality of albums for storing photos of different photo types, the electronic equipment can classify the photos of multiple types by executing the method provided by the embodiment, and the requirement of a user for classifying the photos of multiple types is met.
Meanwhile, the electronic equipment can determine the type of the photo to be stored according to the characteristic information of the photo to be stored, so that the operation of uploading the photo to be stored to a server and receiving the type information of the photo to be stored issued by the server in the related technology is omitted, network dependence and expenditure are reduced, and the processing speed is increased.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.
Drawings
FIG. 1 is a flowchart illustrating a method of classifying photographs in accordance with an exemplary embodiment;
FIG. 2 is a flow diagram illustrating a method of training a type-identifying neural network in accordance with an exemplary embodiment;
FIG. 3 is a block diagram illustrating a photograph sorting apparatus according to one exemplary embodiment;
fig. 4 is a schematic structural diagram of an electronic device shown in accordance with an exemplary embodiment.
Detailed Description
Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The implementations described in the exemplary embodiments below are not intended to represent all implementations consistent with the present disclosure. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present disclosure, as detailed in the appended claims.
The terminology used in the present disclosure is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. As used in this disclosure and the appended claims, the singular forms "a", "an", and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It should also be understood that the term "and/or" as used herein refers to and encompasses any and all possible combinations of one or more of the associated listed items.
It is to be understood that although the terms first, second, third, etc. may be used herein to describe various information, such information should not be limited to these terms. These terms are only used to distinguish one type of information from another. For example, first information may also be referred to as second information, and similarly, second information may also be referred to as first information, without departing from the scope of the present disclosure. The word "if" as used herein may be interpreted as "at" \8230; "or" when 8230; \8230; "or" in response to a determination ", depending on the context.
Fig. 1 is a flowchart illustrating a photo classification method applied to an electronic device according to an exemplary embodiment, where the method illustrated in fig. 1 includes:
in step 101, after a new photo to be stored is detected, feature information of the photo to be stored is obtained, and a photo type of the photo to be stored is determined according to the feature information.
The characteristic information of the photo to be stored is used for determining the photo type of the photo to be stored. The characteristic information of the photo to be stored may include at least one of: the outline information, color information, relative position information, etc. of the photographed object in the photograph are to be stored. The kind of information included in the characteristic information may be set according to need and experience.
The photo type may be a default type of the album or a type set by the user. There are many types of photographs, e.g., people, buildings, landscapes, etc.
In an alternative embodiment, the electronic device may determine the photo type of a newly-taken photo (i.e., a photo to be stored) during the photographing process according to the feature information of the photo.
In an alternative embodiment, the electronic device may determine the photo type of the photo according to the feature information of the photo after receiving the photo (i.e., the photo to be stored) sent by the other device.
In an alternative embodiment, the electronic device is provided with a type recognition neural network, the input of the type recognition neural network is a photo, the output of the type recognition neural network is a photo type of the photo, and the type recognition neural network has a function of determining the photo type of the photo and outputting the photo type according to the feature information of the photo.
Based on the method, the electronic equipment can input the photo to be stored into the type recognition neural network, so that the type recognition neural network obtains the characteristic information of the photo to be stored, determines the photo type of the photo to be stored according to the characteristic information of the photo to be stored, and outputs the photo type of the photo to be stored.
In step 102, according to the photo type of the photo to be stored, whether a target album corresponding to the photo type exists is searched, wherein the target album is a system default album or a custom album created by a user in advance.
The electronic device may include a system default album.
The electronic equipment can provide an interface for a user to create an album, so that the user can create the album and can set the photo types of photos which can be stored in the album.
Some albums included with the electronic device, such as a system default album, may be used to store photos of different photo types. Other albums included with electronic devices, such as custom albums, are used only to store photos of a particular photo type. After acquiring the photo type of the photo to be stored, the electronic equipment searches whether an album for storing the photo of the photo type exists.
For example, the name of an album indicates the photo type of photos that the album uses for storage. The electronic equipment determines whether a target album for storing the photos to be stored exists by matching the photo type indicated by the name of the album and the photo type of the photos to be stored.
As another example, an album is provided with a label that includes the type of photos that the album uses to store. The electronic equipment determines whether a target photo album for storing the photos to be stored exists in the local photo album by matching the photo type included in the photo album label with the photo type of the photos to be stored.
If the photo type matching is successful, the photo album to be matched is called a target photo album, and if the photo type matching is failed, the target photo album does not exist.
In step 103, if the target album exists, the photos to be stored are stored in the target album.
In an optional embodiment, after determining the target album, the electronic device outputs inquiry information, where the inquiry information is used to inquire whether to store the photo to be stored in the target album. After receiving an instruction for storing the photo to be stored in the target album, the electronic equipment stores the photo to be stored in the target album.
For example, a prompt box pops up on an interface of the electronic device, the prompt box displays inquiry information of 'whether to store a photo to be stored in a target album', the prompt box is provided with a button 'yes' and a button 'no', if the selection operation of the button 'yes' by a user is received, a determination instruction is received, the photo to be stored is stored in the target album, if the selection operation of the button 'no' by the user is received, a rejection instruction is received, the photo to be stored is not stored in the target album, and in this case, the photo to be stored may not be stored.
In this embodiment, the electronic device outputs the query information after determining the target album, and stores the photo to be stored in the target album after the user confirms the query information. Through user confirmation, the photos are guaranteed to be stored in the correct photo album, and the problem that the photos are stored in the wrong photo album due to the fact that the electronic equipment recognizes the wrong type is avoided.
In one embodiment, the electronic device may output the reminder after determining that the target album does not exist. For example, a prompt message "album not found for storing the target category" and/or a prompt message "album newly created for storing photos of the target category" is output. The target category is the category of the photo to be stored. The user can use the electronic equipment to create an album for storing the photos to be stored and the photos of the same category according to the prompt message.
In this embodiment, the electronic device has a function of outputting the prompt message after determining that the target album does not exist, so that a user can conveniently establish an album corresponding to the target category in time and store the photos to be stored into the album corresponding to the target category.
The embodiment of the disclosure provides a photo classification method, wherein after detecting a new photo to be stored, an electronic device acquires feature information of the photo to be stored, determines a photo type of the photo to be stored according to the feature information, and stores the photo to be stored in a target album corresponding to the photo type. The electronic equipment classifies the photos according to the characteristic information of the photos to be stored and the photo albums for storing different photo types, so that the photo classifying mode of the electronic equipment is enriched.
When the electronic equipment comprises a plurality of albums for storing photos of different photo types, the electronic equipment can classify the photos of multiple types by executing the method provided by the embodiment, and the requirement of a user on classifying the photos of multiple types is met.
Meanwhile, the electronic equipment can determine the type of the photo to be stored according to the characteristic information of the photo to be stored, so that the operation of uploading the photo to be stored to a server and receiving the type information of the photo to be stored issued by the server in the related technology is omitted, network dependence and expenditure are reduced, and the processing speed is increased.
Fig. 2 is a flowchart illustrating a training method of a type recognition neural network according to an exemplary embodiment, and referring to fig. 2, the training method of the type recognition neural network includes:
in step 201, in response to an operation of saving the sample photo to the custom album, obtaining annotation information set for the sample photo, where the annotation information includes preset type information of the custom album.
Defining preset type information of the custom album: the custom album is used for storing photos of preset photo types.
There are various ways to acquire the annotation information set for the sample photograph. For example, the annotation information carried by the sample photo includes preset type information of the custom album, and the electronic device obtains the annotation information carried by the sample photo.
In another example, preset type information is set for the custom album. And setting a label for the custom photo album, wherein the label comprises preset type information of the custom photo album.
The sample photo is stored in the custom photo album, the marking information set for the sample photo is obtained, and the standard information comprises the preset type information of the custom photo album, which can be understood as obtaining the preset type information set for the custom photo album.
In step 202, the sample photo is input into the recognition network to be trained, so that the recognition network to be trained outputs the actual type information recognized for the sample photo.
In step 203, based on the difference between the preset type information and the actual type information, parameters in the recognition network to be trained are adjusted to obtain a type recognition neural network.
There are various ways to adjust the parameters. For example, the parameters in the recognition network to be trained may be adjusted based on the difference between the preset type information and the actual type information until the difference is minimum, or the adjustment may be stopped until the difference is less than or equal to the difference threshold.
For another example, the parameters in the recognition network to be trained may be adjusted based on the difference between the preset type information and the actual type information until the preset number of times is adjusted, and the adjustment is stopped.
In an optional embodiment, to improve the recognition performance of the type recognition neural network, after the type recognition neural network is generated, parameters in the type recognition neural network may be adjusted in the process of applying the type recognition neural network, so as to further improve the accuracy of the result output by the type recognition neural network.
In the process of applying the type recognition neural network, the electronic equipment can adjust the parameters of the type recognition neural network according to the current stored photos in the custom album and the preset type information of the custom album under the condition of meeting the network adjustment condition, so that the dynamic adjustment of the type recognition neural network is realized.
There are various network adjustment conditions. For example, 1, a condition is set for the total number of the currently stored photos in the custom album, and if the total number of the currently stored photos in the custom album reaches a preset number, it is determined that a network adjustment condition is met; 2. setting conditions aiming at the number of the newly added photos in the custom album, and determining that network adjustment conditions are met if the number of the newly added photos in the custom album reaches a preset value; 3. aiming at the condition set by the parameter adjusting interval time, if the interval time from the last network parameter adjustment reaches the preset interval time, determining that the network adjusting condition is met; 4. and aiming at the conditions set by the user instruction, if the electronic equipment receives a parameter adjusting instruction sent by the user, determining that the network adjusting conditions are met. The present embodiment merely describes the network adjustment condition by way of example, and is not limited thereto.
While, for purposes of simplicity of explanation, the foregoing method embodiments have been described as a series of acts or combination of acts, it will be appreciated by those skilled in the art that the present disclosure is not limited by the order of acts, as some steps may, in accordance with the present disclosure, occur in other orders and concurrently.
Further, those skilled in the art should also appreciate that the embodiments described in the specification are exemplary embodiments and that acts and modules referred to are not necessarily required by the disclosure.
Corresponding to the embodiment of the application function implementation method, the disclosure also provides an application function implementation device and a corresponding embodiment.
Fig. 3 is a block diagram illustrating a photo sorting apparatus according to an exemplary embodiment, referring to fig. 3, the apparatus is applied to an electronic device, and the apparatus includes:
the photo type determining module 31 is configured to, after a new photo to be stored is detected, obtain feature information of the photo to be stored, and determine a photo type of the photo to be stored according to the feature information;
a target album searching module 32 configured to search whether a target album corresponding to the type of the photo exists according to the type of the photo, wherein the target album is a system default album or a custom album created by a user in advance;
and the photo storage module 33 is configured to store the photos to be stored into the target album if the target album exists.
In an alternative embodiment, on the basis of the photo classification apparatus shown in fig. 3, the apparatus may further include:
and the album generating module is configured to generate a corresponding custom album according to the creation instruction after receiving the creation instruction for newly creating the album.
In an alternative embodiment, the photo type determining module 31 may be configured to input the photo to be stored into a type recognition neural network, so that the type recognition neural network obtains the feature information of the photo to be stored, determines the type of the photo according to the feature information, and outputs the type of the photo.
In an optional embodiment, the apparatus may further comprise:
the annotation information acquisition module is configured to respond to an operation of storing a sample photo in the custom album, and acquire annotation information set for the sample photo, wherein the annotation information comprises preset type information of the custom album;
the sample photo input module is configured to input the sample photo into a recognition network to be trained so that the recognition network to be trained outputs actual type information obtained by recognition of the sample photo;
a first parameter adjusting module configured to adjust parameters in the recognition network to be trained based on a difference between the preset type information and the actual type information to obtain the type recognition neural network.
In an optional embodiment, the apparatus may further comprise:
and the second parameter adjusting module is configured to adjust the parameters in the type recognition neural network according to the pictures currently stored in the custom album and the preset type information in response to the network adjusting condition being met.
In an alternative embodiment, on the basis of the photo classification apparatus shown in fig. 3, the apparatus may further include:
the query information output module is configured to output query information after the target photo album is determined, wherein the query information is used for querying whether the photo to be stored is stored in the target photo album;
the photo storage module is configured to store the photo to be stored into the target album in response to receiving an instruction for storing the photo to be stored into the target album.
In an alternative embodiment, on the basis of the photo classification apparatus shown in fig. 3, the apparatus may further include:
a prompt information output module configured to output a prompt information in response to the absence of the target album.
Fig. 4 is a schematic diagram of a structure of an electronic device 1600 shown in accordance with an example embodiment. For example, the electronic device 1600 may be a user device, which may be embodied as a mobile phone, a computer, a digital broadcast, a messaging device, a gaming console, a tablet device, a medical device, a fitness device, a personal digital assistant, a wearable device such as a smart watch, smart glasses, a smart bracelet, a smart running shoe, and the like.
Referring to fig. 4, electronic device 1600 may include one or more of the following components: processing component 1602, memory 1604, power component 1606, multimedia component 1608, audio component 1610, input/output (I/O) interface 1612, sensor component 1614, and communications component 1616.
The processing component 1602 generally controls overall operation of the electronic device 1600, such as operations associated with display, telephone calls, data communications, camera operations, and recording operations. The processing component 1602 may include one or more processors 1620 to execute instructions to perform all or part of the steps of the methods described above. Further, the processing component 1602 can include one or more modules that facilitate interaction between the processing component 1602 and other components. For example, the processing component 1602 can include a multimedia module to facilitate interaction between the multimedia component 1608 and the processing component 1602.
The memory 1604 is configured to store various types of data to support operation at the device 1600. Examples of such data include instructions for any application or method operating on the electronic device 1600, contact data, phonebook data, messages, pictures, videos, and so forth. The memory 1604 may be implemented by any type of volatile or non-volatile memory device or combination thereof, such as Static Random Access Memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic or optical disk.
The power supply component 1606 provides power to various components of the electronic device 1600. The power components 1606 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power for the electronic device 1600.
The multimedia component 1608 includes a screen that provides an output interface between the electronic device 1600 and a user as described above. In some embodiments, the screen may include a Liquid Crystal Display (LCD) and a Touch Panel (TP). If the screen includes a touch panel, the screen may be implemented as a touch screen to receive an input signal from a user. The touch panel includes one or more touch sensors to sense touch, slide, and gestures on the touch panel. The touch sensor may not only sense the boundary of the touch or slide action but also detect the duration and pressure associated with the touch or slide operation. In some embodiments, the multimedia component 1608 comprises a front-facing camera and/or a rear-facing camera. The front-facing camera and/or the back-facing camera may receive external multimedia data when device 1600 is in an operational mode, such as an adjustment mode or a video mode. Each front camera and rear camera may be a fixed optical lens system or have a focal length and optical zoom capability.
The audio component 1610 is configured to output and/or input an audio signal. For example, the audio component 1610 includes a Microphone (MIC) configured to receive external audio signals when the electronic device 1600 is in an operational mode, such as a call mode, a recording mode, and a voice recognition mode. The received audio signal may further be stored in the memory 1604 or transmitted via the communications component 1616. In some embodiments, audio component 1610 further comprises a speaker for outputting audio signals.
The I/O interface 1612 provides an interface between the processing component 1602 and a peripheral interface module, which can be a keyboard, click wheel, button, or the like. These buttons may include, but are not limited to: a home button, a volume button, a start button, and a lock button.
Sensor assembly 1614 includes one or more sensors for providing various aspects of status assessment for electronic device 1600. For example, sensor assembly 1614 may detect an open/closed state of device 1600, the relative positioning of components, such as a display and keypad of device 1600, a change in position of device 1600 or a component of device 1600, the presence or absence of user contact with device 1600, orientation or acceleration/deceleration of device 1600, and a change in temperature of device 1600. The sensor assembly 1614 may include a proximity sensor configured to detect the presence of a nearby object without any physical contact. The sensor assembly 1614 may also include a light sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, the sensor assembly 1614 may also include an acceleration sensor, a gyroscope sensor, a magnetic sensor, a pressure sensor, or a temperature sensor.
The communications component 1616 is configured to facilitate communications between the electronic device 1600 and other devices in a wired or wireless manner. The electronic device 1600 may access a wireless network based on a communication standard, such as WiFi,2G, or 3G, or a combination thereof. In an exemplary embodiment, the communication component 1616 receives broadcast signals or broadcast related information from an external broadcast management system via a broadcast channel. In an exemplary embodiment, the aforementioned communications component 1616 further includes a Near Field Communication (NFC) module to facilitate short-range communications. For example, the NFC module may be implemented based on Radio Frequency Identification (RFID) technology, infrared data association (IrDA) technology, ultra Wideband (UWB) technology, bluetooth (BT) technology, and other technologies.
In an exemplary embodiment, the electronic device 1600 may be implemented by one or more Application Specific Integrated Circuits (ASICs), digital Signal Processors (DSPs), digital Signal Processing Devices (DSPDs), programmable Logic Devices (PLDs), field Programmable Gate Arrays (FPGAs), controllers, micro-controllers, microprocessors or other electronic components for performing the above-described methods.
In an exemplary embodiment, there is also provided a non-transitory computer-readable storage medium, such as the memory 1604 including instructions that, when executed by the processor 1620 of the electronic device 1600, enable the electronic device 1600 to perform a photo classification method, the method comprising: when a new photo to be stored is detected, acquiring the characteristic information of the photo to be stored, and determining the photo type of the photo to be stored according to the characteristic information; searching whether a target photo album corresponding to the photo type exists or not according to the photo type, wherein the target photo album is a system default photo album or a custom photo album created by a user in advance; and if the target photo album exists, storing the photo to be stored into the target photo album.
The non-transitory computer readable storage medium may be a ROM, a Random Access Memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, and the like.
Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the invention disclosed herein. This disclosure is intended to cover any variations, uses, or adaptations of the disclosure following, in general, the principles of the disclosure and including such departures from the present disclosure as come within known or customary practice in the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.
It will be understood that the present disclosure is not limited to the precise arrangements described above and shown in the drawings and that various modifications and changes may be made without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.

Claims (16)

1. A photo classification method applied to an electronic device, the method comprising:
when a new photo to be stored is detected, acquiring the characteristic information of the photo to be stored, and determining the type of the photo to be stored according to the characteristic information;
searching whether a target album corresponding to the photo type exists or not according to the photo type, wherein the target album is a system default album or a custom album pre-created by a user;
and if the target photo album exists, storing the photo to be stored into the target photo album.
2. The method of claim 1, further comprising:
and after receiving a creation instruction for creating the photo album, generating a corresponding custom photo album according to the creation instruction.
3. The method according to claim 1, wherein the obtaining of the feature information of the photo to be stored and the determining of the photo type of the photo to be stored according to the feature information comprises:
inputting the photo to be stored into a type recognition neural network, enabling the type recognition neural network to acquire the feature information of the photo to be stored, determining the type of the photo according to the feature information, and outputting the type of the photo.
4. The method of claim 3, wherein the generation of the type-identifying neural network comprises:
responding to an operation of storing a sample photo in the custom photo album, and acquiring marking information set for the sample photo, wherein the marking information comprises preset type information of the custom photo album;
inputting the sample photo into a recognition network to be trained so that the recognition network to be trained outputs actual type information obtained by recognition of the sample photo;
and adjusting parameters in the recognition network to be trained based on the difference between the preset type information and the actual type information to obtain the type recognition neural network.
5. The method of claim 4, further comprising:
and responding to the condition of meeting network adjustment, and adjusting parameters in the type recognition neural network according to the current stored photos in the custom album and the preset type information.
6. The method of claim 1, further comprising:
and after the target album is determined, outputting inquiry information, wherein the inquiry information is used for inquiring whether the photo to be stored is stored in the target album, and responding to a received instruction for storing the photo to be stored in the target album, and storing the photo to be stored in the target album.
7. The method of claim 1, further comprising:
and responding to the target album not existing, and outputting prompt information.
8. A photo classification device applied to an electronic device, the device comprising:
the photo type determining module is configured to acquire the feature information of the photo to be stored after a new photo to be stored is detected, and determine the photo type of the photo to be stored according to the feature information;
the target album searching module is configured to search whether a target album corresponding to the photo type exists or not according to the photo type, wherein the target album is a system default album or a custom album created by a user in advance;
and the photo storage module is configured to store the photo to be stored into the target photo album if the target photo album exists.
9. The apparatus of claim 8, further comprising:
and the album generating module is configured to generate a corresponding custom album according to the creation instruction after receiving the creation instruction for newly creating the album.
10. The apparatus of claim 8, wherein:
the photo type determining module is configured to input the photo to be stored into a type recognition neural network, so that the type recognition neural network acquires the feature information of the photo to be stored, determines the photo type according to the feature information, and outputs the photo type.
11. The apparatus of claim 10, further comprising:
the annotation information acquisition module is configured to respond to an operation of storing a sample photo in the custom album, and acquire annotation information set for the sample photo, wherein the annotation information comprises preset type information of the custom album;
the sample photo input module is configured to input the sample photo into a recognition network to be trained so that the recognition network to be trained outputs actual type information recognized by aiming at the sample photo;
and the first parameter adjusting module is configured to adjust parameters in the recognition network to be trained based on the difference between the preset type information and the actual type information to obtain the type recognition neural network.
12. The apparatus of claim 11, further comprising:
and the second parameter adjusting module is configured to adjust the parameters in the type recognition neural network according to the current stored pictures in the custom album and the preset type information in response to the network adjusting condition being met.
13. The apparatus of claim 8, further comprising:
the query information output module is configured to output query information after the target photo album is determined, wherein the query information is used for querying whether the photo to be stored is stored in the target photo album;
the photo storage module is configured to store the photo to be stored into the target album in response to receiving an instruction for storing the photo to be stored into the target album.
14. The apparatus of claim 8, further comprising:
a prompt information output module configured to output a prompt information in response to the target album not being present.
15. A non-transitory computer readable storage medium having stored thereon a computer program, characterized in that the program, when executed by a processor, implements the method of any one of claims 1-7.
16. An electronic device, comprising:
a processor;
a memory for storing processor-executable instructions;
wherein the processor is configured to execute the instructions to implement the method of any one of claims 1-7.
CN202110586187.8A 2021-05-27 2021-05-27 Photo classification method and device Pending CN115481271A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202110586187.8A CN115481271A (en) 2021-05-27 2021-05-27 Photo classification method and device

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202110586187.8A CN115481271A (en) 2021-05-27 2021-05-27 Photo classification method and device

Publications (1)

Publication Number Publication Date
CN115481271A true CN115481271A (en) 2022-12-16

Family

ID=84419600

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202110586187.8A Pending CN115481271A (en) 2021-05-27 2021-05-27 Photo classification method and device

Country Status (1)

Country Link
CN (1) CN115481271A (en)

Similar Documents

Publication Publication Date Title
EP3179408B1 (en) Picture processing method and apparatus, computer program and recording medium
US9953212B2 (en) Method and apparatus for album display, and storage medium
US10141025B2 (en) Method, device and computer-readable medium for adjusting video playing progress
US10115019B2 (en) Video categorization method and apparatus, and storage medium
CN104572905A (en) Photo index creation method, photo searching method and devices
CN106095465B (en) Method and device for setting identity image
CN109255128B (en) Multi-level label generation method, device and storage medium
CN109961094B (en) Sample acquisition method and device, electronic equipment and readable storage medium
CN106202194A (en) The storage method and device of screenshot picture
CN107423386B (en) Method and device for generating electronic card
US11335348B2 (en) Input method, device, apparatus, and storage medium
CN106547850B (en) Expression annotation method and device
CN106453528A (en) Method and device for pushing message
CN107801282A (en) Desk lamp, desk lamp control method and device
CN112004020B (en) Image processing method, image processing device, electronic equipment and storage medium
CN108027821B (en) Method and device for processing picture
CN105260088B (en) Information classification display processing method and device
CN110941727A (en) Resource recommendation method and device, electronic equipment and storage medium
CN110929771B (en) Image sample classification method and device, electronic equipment and readable storage medium
CN115481271A (en) Photo classification method and device
CN108769513B (en) Camera photographing method and device
CN113870195A (en) Target map detection model training and map detection method and device
CN112825544A (en) Picture processing method and device and storage medium
CN112822389B (en) Photograph shooting method, photograph shooting device and storage medium
CN113407754B (en) Album generating method, apparatus, electronic device and storage medium

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination