CN111784712A

CN111784712A - Image processing method, device, equipment and computer readable medium

Info

Publication number: CN111784712A
Application number: CN202010694038.9A
Authority: CN
Inventors: 王旭
Original assignee: Beijing ByteDance Network Technology Co Ltd
Current assignee: Beijing ByteDance Network Technology Co Ltd
Priority date: 2020-07-17
Filing date: 2020-07-17
Publication date: 2020-10-16
Anticipated expiration: 2040-07-17
Also published as: CN111784712B

Abstract

The embodiment of the disclosure discloses an image processing method, an image processing device, an electronic device and a computer readable medium. One embodiment of the method comprises: acquiring a target image and a mark of a user for a target object in the target image; determining a first probability that each pixel point in the target image is a target pixel point based on the target image and the mark; and extracting a first image area corresponding to the target object based at least in part on the first probability. The embodiment realizes the determination of a smaller target area in the target image by combining the general matting technology and the matting technology based on user interaction.

Description

Image processing method, device, equipment and computer readable medium

Technical Field

Embodiments of the present disclosure relate to the field of computer technologies, and in particular, to an image processing method, an apparatus, a device, and a computer-readable medium.

Background

Matting is a common image processing technique. For determining a target area in the target image for further image processing of the target area. The general matting technology always filters out smaller target areas, and the purpose of determining the smaller target areas in the target images cannot be achieved.

Disclosure of Invention

This summary is provided to introduce a selection of concepts in a simplified form that are further described below in the detailed description. This summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used to limit the scope of the claimed subject matter.

Some embodiments of the present disclosure propose an image processing method, apparatus, device and computer readable medium to solve the technical problems mentioned in the background section above.

In a first aspect, some embodiments of the present disclosure provide an image processing method, including: acquiring a target image and a mark of a user for a target object in the target image; determining a first probability that each pixel point in the target image is a target pixel point based on the target image and the mark; and extracting a first image area corresponding to the target object based at least in part on the first probability.

In a second aspect, some embodiments of the present disclosure provide an image processing apparatus comprising: an acquisition unit configured to acquire a target image and a mark of a user for a target object in the target image; a determining unit configured to determine a first probability that each pixel in the target image is a target pixel based on the target image and the mark; an extraction unit configured to extract a first image region corresponding to the target object based at least in part on the first probability.

In a third aspect, some embodiments of the present disclosure provide an electronic device, comprising: one or more processors; a storage device having one or more programs stored thereon, which when executed by one or more processors, cause the one or more processors to implement the method as described in any of the implementations of the first aspect.

In a fourth aspect, some embodiments of the disclosure provide a computer readable medium having a computer program stored thereon, where the program when executed by a processor implements a method as described in any of the implementations of the first aspect.

One of the above-described various embodiments of the present disclosure has the following advantageous effects: determining a smaller target area in a target image is achieved by combining a generic matting technique and a matting technique based on user interaction.

Drawings

The above and other features, advantages and aspects of various embodiments of the present disclosure will become more apparent by referring to the following detailed description when taken in conjunction with the accompanying drawings. Throughout the drawings, the same or similar reference numbers refer to the same or similar elements. It should be understood that the drawings are schematic and that elements and elements are not necessarily drawn to scale.

Fig. 1 is a schematic diagram of one application scenario of an image processing method of some embodiments of the present disclosure;

FIG. 2 is a flow diagram of some embodiments of an image processing method according to the present disclosure;

FIG. 3 is a flow diagram of further embodiments of an image processing method according to the present disclosure;

FIG. 4 is a schematic block diagram of some embodiments of an image processing apparatus according to the present disclosure;

FIG. 5 is a schematic structural diagram of an electronic device suitable for use in implementing some embodiments of the present disclosure.

Detailed Description

Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While certain embodiments of the present disclosure are shown in the drawings, it is to be understood that the disclosure may be embodied in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided for a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the disclosure are for illustration purposes only and are not intended to limit the scope of the disclosure.

It should be noted that, for convenience of description, only the portions related to the related invention are shown in the drawings. The embodiments and features of the embodiments in the present disclosure may be combined with each other without conflict.

It should be noted that the terms "first", "second", and the like in the present disclosure are only used for distinguishing different devices, modules or units, and are not used for limiting the order or interdependence relationship of the functions performed by the devices, modules or units.

It is noted that references to "a", "an", and "the" modifications in this disclosure are intended to be illustrative rather than limiting, and that those skilled in the art will recognize that "one or more" may be used unless the context clearly dictates otherwise.

The names of messages or information exchanged between devices in the embodiments of the present disclosure are for illustrative purposes only, and are not intended to limit the scope of the messages or information.

The present disclosure will be described in detail below with reference to the accompanying drawings in conjunction with embodiments.

Fig. 1 shows a schematic diagram of one application scenario in which the image processing method of some embodiments of the present disclosure may be applied.

In the application scenario shown in fig. 1, first, the computing device 101 may obtain a target image 102 and a user's label 103 for a target object in the target image. In the present application scene, the target image is an image showing a mountain and the sun. On this basis, the target object may be an image of a mountain in the target image. Thereafter, the computing device 101 may determine a first probability 104 that each pixel in the target image 102 is a target pixel. Finally, a first image region 105 corresponding to the target object is extracted based at least in part on the first probability 104.

The computing device 101 may be hardware or software. When the computing device is hardware, it may be implemented as a distributed cluster composed of a plurality of servers or electronic devices, or may be implemented as a single server or a single electronic device. When the computing device is embodied as software, it may be implemented as multiple pieces of software or software modules, for example, to provide distributed services, or as a single piece of software or software module. And is not particularly limited herein.

It should be understood that the number of computing devices 101 in FIG. 1 is merely illustrative. There may be any number of computing devices 101, as desired for implementation.

With continued reference to fig. 2, a flow 200 of some embodiments of an image processing method according to the present disclosure is shown. The image processing method comprises the following steps:

step 201, acquiring a target image and a mark of a user for a target object in the target image.

In some embodiments, an executing subject of the image processing method (e.g., the computing device shown in fig. 1) may acquire the target image and the mark of the user for the target object in the target image through a wired connection or a wireless connection. It should be noted that the wireless connection means may include, but is not limited to, a 3G/4G connection, a WiFi connection, a bluetooth connection, a WiMAX connection, a Zigbee connection, a uwb (ultra wideband) connection, and other wireless connection means now known or developed in the future.

In some embodiments, the mark may be a result of any user interaction with the target object. For example, handwriting for the target image is completed by the user using a mouse.

Step 202, determining a first probability that each pixel point in the target image is a target pixel point based on the target image and the mark.

In some embodiments, according to actual needs, the first probability of the pixel point corresponding to the user mark may be determined as 1. And determining the first probability of the pixel points in the target image as 0.

In some optional implementation manners of some embodiments, the executing body may further determine, by using a matting algorithm, a first probability that each pixel point in the target image is a target pixel point based on the target image and the mark. Wherein, the matting algorithm comprises matting algorithm.

Step 203, extracting a first image region corresponding to the target object based at least in part on the first probability.

In some embodiments, the executing entity may first determine an image region composed of pixels having the first probability exceeding a preset threshold as a first image region of the target object, and then extract the first image region.

In some optional implementations of some embodiments, the executing body may extract the first image region corresponding to the target object by:

step one, based on the target image and the mark, determining a first probability that each pixel point in the target image is a target pixel point.

And secondly, carrying out image segmentation in a second image area containing the user mark to obtain a second probability that each pixel point in the second image area is a target pixel point.

And step three, extracting a first image area formed by target pixel points in the target image determined based on the first probability and the second probability.

In some embodiments, the executing entity may determine, as the target pixel point, a pixel point of which the sum of the first probability and the second probability is higher than a first preset threshold.

In some embodiments, the execution subject may first determine a higher one of the first probability and the second probability as a third probability. And then, determining the pixel points with the third probability higher than a second preset threshold value as target pixel points.

In some embodiments, the executing entity may further determine, as the target pixel, a pixel in which an average value of the first probability and the second probability is higher than a third preset threshold.

Some embodiments of the present disclosure provide methods that enable the determination of smaller target regions in a target image by combining generic matting techniques and user interaction based matting techniques.

With further reference to fig. 3, a flow 300 of further embodiments of an image processing method is shown. The flow 300 of the image processing method comprises the following steps:

step 301, acquiring a target image and a mark of a user for a target object in the target image.

In some embodiments, the specific implementation of step 301 and the technical effect thereof may refer to step 201 in the embodiment corresponding to fig. 2, and are not described herein again.

Step 302, determining a first probability that each pixel point in the target image is a target pixel point based on the target image and the mark.

Step 303, performing image segmentation on the second image region containing the user mark to obtain a second probability that each pixel point in the second image region is a target pixel point.

In some embodiments, image segmentation may be performed in a second image region including the user identifier by using image processing software or an online image processing tool, so as to obtain a second probability that each pixel point in the second image region is a target pixel point.

In some optional implementation manners of some embodiments, the executing body may further input the second image region to an image segmentation network, so as to obtain a second probability that each pixel point in the second image region is a target pixel point.

In some embodiments, the image segmentation network may be an existing image segmentation network that is acquired in advance.

In some optional implementations of some embodiments, the image segmentation network may be further obtained by:

the method comprises the steps of firstly, obtaining a sample, wherein the sample comprises a sample image and a labeled sample image corresponding to the sample image.

And step two, inputting the sample image into a model to be trained to obtain an image labeled by the model.

And step three, analyzing the image marked by the model and the marked sample image, and determining the loss value of the image marked by the model.

And step four, comparing the loss value with a target value, and determining whether the model to be trained is trained according to a comparison result.

And step five, responding to the fact that the model to be trained is not trained, and adjusting relevant parameters in the model to be trained.

Step 304, extracting the first image region based on the first probability and the second probability, wherein the first image region is a set of target pixels.

As can be seen from fig. 3, compared to the description of some embodiments corresponding to fig. 2, the flow 300 of the image processing method in some embodiments corresponding to fig. 3 embodies the steps of determining the first probability and the second probability. Thus, the solutions described in these embodiments can thereby determine the first image region more accurately.

With further reference to fig. 4, as an implementation of the methods shown in the above figures, the present disclosure provides some embodiments of an image processing apparatus, which correspond to those shown in fig. 2, and which may be applied in particular in various electronic devices.

As shown in fig. 4, an image processing apparatus 400 of some embodiments includes: an acquisition unit 401, a determination unit 402, and an extraction unit 403. The acquiring unit 401 is configured to acquire a target image and a mark of a user for a target object in the target image; a determining unit 402 configured to determine a first probability that each pixel in the target image is a target pixel based on the target image and the mark; an extracting unit 403 configured to extract a first image region corresponding to the target object based at least in part on the first probability.

In an optional implementation of some embodiments, the extraction unit is further configured to: performing image segmentation in a second image region containing the user mark to obtain a second probability that each pixel point in the second image region is a target pixel point; and extracting the first image area based on the first probability and the second probability, wherein the first image area is a set of target pixel points.

In an optional implementation of some embodiments, the apparatus further comprises: a magnification unit configured to respond to a determination that the size of the target object is less than a predetermined threshold; and amplifying and presenting a first image area corresponding to the target object.

In an optional implementation of some embodiments, the extraction unit is further configured to: and determining the first probability that each pixel point in the target image is a target pixel point by using a matting algorithm based on the target image and the mark.

In an optional implementation of some embodiments, the extraction unit is further configured to: and inputting the second image area into an image segmentation network to obtain a second probability that each pixel point in the second image area is a target pixel point.

In an optional implementation manner of some embodiments, the image segmentation network in the image segmentation unit is obtained by: obtaining a sample, wherein the sample comprises a sample image and a labeled sample image corresponding to the sample image; inputting the sample image into a model to be trained to obtain an image labeled by the model; analyzing the image marked by the model and the marked sample image to determine the loss value of the image marked by the model; comparing the loss value with a target value, and determining whether the model to be trained is trained according to a comparison result; and adjusting relevant parameters in the model to be trained in response to the fact that the model to be trained is not trained completely.

In an optional implementation manner of some embodiments, the step of obtaining the image segmentation network in the image segmentation unit further includes: and determining the model to be trained as the image segmentation network in response to determining that the training of the model to be trained is completed.

It will be understood that the elements described in the apparatus 400 correspond to various steps in the method described with reference to fig. 2. Thus, the operations, features and resulting advantages described above with respect to the method are also applicable to the apparatus 400 and the units included therein, and will not be described herein again.

Referring now to fig. 5, a schematic diagram of an electronic device (e.g., the server or terminal device of fig. 1) 500 suitable for use in implementing some embodiments of the present disclosure is shown. The electronic device in some embodiments of the present disclosure may include, but is not limited to, a mobile terminal such as a mobile phone, a notebook computer, a digital broadcast receiver, a PDA (personal digital assistant), a PAD (tablet computer), a PMP (portable multimedia player), a vehicle-mounted terminal (e.g., a car navigation terminal), and the like, and a stationary terminal such as a digital TV, a desktop computer, and the like. The electronic device shown in fig. 5 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present disclosure.

As shown in fig. 5, electronic device 500 may include a processing means (e.g., central processing unit, graphics processor, etc.) 501 that may perform various appropriate actions and processes in accordance with a program stored in a Read Only Memory (ROM)502 or a program loaded from a storage means 508 into a Random Access Memory (RAM) 503. In the RAM503, various programs and data necessary for the operation of the electronic apparatus 500 are also stored. The processing device 501, the ROM 502, and the RAM503 are connected to each other through a bus 504. An input/output (I/O) interface 505 is also connected to bus 504.

Generally, the following devices may be connected to the I/O interface 505: input devices 506 including, for example, a touch screen, touch pad, keyboard, mouse, camera, microphone, accelerometer, gyroscope, etc.; output devices 507 including, for example, a Liquid Crystal Display (LCD), speakers, vibrators, and the like; storage devices 508 including, for example, magnetic tape, hard disk, etc.; and a communication device 509. The communication means 509 may allow the electronic device 500 to communicate with other devices wirelessly or by wire to exchange data. While fig. 5 illustrates an electronic device 500 having various means, it is to be understood that not all illustrated means are required to be implemented or provided. More or fewer devices may alternatively be implemented or provided. Each block shown in fig. 5 may represent one device or may represent multiple devices as desired.

In particular, according to some embodiments of the present disclosure, the processes described above with reference to the flow diagrams may be implemented as computer software programs. For example, some embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method illustrated in the flow chart. In some such embodiments, the computer program may be downloaded and installed from a network via the communication means 509, or installed from the storage means 508, or installed from the ROM 502. The computer program, when executed by the processing device 501, performs the above-described functions defined in the methods of some embodiments of the present disclosure.

It should be noted that the computer readable medium described in some embodiments of the present disclosure may be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In some embodiments of the disclosure, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In some embodiments of the present disclosure, however, a computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, optical cables, RF (radio frequency), etc., or any suitable combination of the foregoing.

In some embodiments, the clients, servers may communicate using any currently known or future developed network protocol, such as HTTP (HyperText transfer protocol), and may be interconnected with any form or medium of digital data communication (e.g., a communications network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), the Internet (e.g., the Internet), and peer-to-peer networks (e.g., ad hoc peer-to-peer networks), as well as any currently known or future developed network.

The computer readable medium may be embodied in the electronic device; or may exist separately without being assembled into the electronic device. The computer readable medium carries one or more programs which, when executed by the electronic device, cause the electronic device to: acquiring a target image and a mark of a user for a target object in the target image; determining a first probability that each pixel point in the target image is a target pixel point based on the target image and the mark; and extracting a first image area corresponding to the target object based at least in part on the first probability.

Computer program code for carrying out operations for embodiments of the present disclosure may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + +, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).

The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.

The units described in some embodiments of the present disclosure may be implemented by software, and may also be implemented by hardware. The described units may also be provided in a processor, and may be described as: a processor includes an acquisition unit, an extraction unit. The names of these units do not in some cases constitute a limitation on the unit itself, and for example, the acquisition unit may also be described as a "unit that acquires a target image".

The functions described herein above may be performed, at least in part, by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: field Programmable Gate Arrays (FPGAs), Application Specific Integrated Circuits (ASICs), Application Specific Standard Products (ASSPs), systems on a chip (SOCs), Complex Programmable Logic Devices (CPLDs), and the like.

According to one or more embodiments of the present disclosure, there is provided an image processing method including: acquiring a target image and a mark of a user for a target object in the target image; determining a first probability that each pixel point in the target image is a target pixel point based on the target image and the mark; and extracting a first image area corresponding to the target object based at least in part on the first probability.

According to one or more embodiments of the present disclosure, extracting a first image region corresponding to the target object includes: performing image segmentation in a second image region containing the user mark to obtain a second probability that each pixel point in the second image region is a target pixel point; and extracting the first image area based on the first probability and the second probability, wherein the first image area is a set of target pixel points.

According to one or more embodiments of the present disclosure, after extracting the first image region corresponding to the target object, the method further includes: in response to determining that the size of the target object is less than a predetermined threshold; and amplifying and presenting a first image area corresponding to the target object.

According to one or more embodiments of the present disclosure, determining, based on the target image and the mark, a first probability that each pixel in the target image is a target pixel includes: and determining the first probability that each pixel point in the target image is a target pixel point by using a matting algorithm based on the target image and the mark.

According to one or more embodiments of the present disclosure, performing image segmentation in a second image region including the user identifier to obtain a second probability that each pixel point in the second image region is a target pixel point, includes: and inputting the second image area into an image segmentation network to obtain a second probability that each pixel point in the second image area is a target pixel point.

According to one or more embodiments of the present disclosure, an image segmentation network is obtained by: obtaining a sample, wherein the sample comprises a sample image and a labeled sample image corresponding to the sample image; inputting the sample image into a model to be trained to obtain an image labeled by the model; analyzing the image marked by the model and the marked sample image to determine the loss value of the image marked by the model; comparing the loss value with a target value, and determining whether the model to be trained is trained according to a comparison result; and adjusting relevant parameters in the model to be trained in response to the fact that the model to be trained is not trained completely.

According to one or more embodiments of the present disclosure, the steps further comprise: and determining the model to be trained as the image segmentation network in response to determining that the training of the model to be trained is completed.

According to one or more embodiments of the present disclosure, there is provided an image processing apparatus including: an acquisition unit configured to acquire a target image and a mark of a user for a target object in the target image; a determining unit configured to determine a first probability that each pixel in the target image is a target pixel based on the target image and the mark; an extraction unit configured to extract a first image region corresponding to the target object based at least in part on the first probability.

According to one or more embodiments of the present disclosure, the extraction unit is further configured to: performing image segmentation in a second image region containing the user mark to obtain a second probability that each pixel point in the second image region is a target pixel point; and extracting the first image area based on the first probability and the second probability, wherein the first image area is a set of target pixel points.

According to one or more embodiments of the present disclosure, an apparatus further comprises: a magnification unit configured to respond to a determination that the size of the target object is less than a predetermined threshold; and amplifying and presenting a first image area corresponding to the target object.

According to one or more embodiments of the present disclosure, the extraction unit is further configured to: and determining the first probability that each pixel point in the target image is a target pixel point by using a matting algorithm based on the target image and the mark.

According to one or more embodiments of the present disclosure, the extraction unit is further configured to: and inputting the second image area into an image segmentation network to obtain a second probability that each pixel point in the second image area is a target pixel point.

According to one or more embodiments of the present disclosure, the image segmentation network in the image segmentation unit is obtained by: obtaining a sample, wherein the sample comprises a sample image and a labeled sample image corresponding to the sample image; inputting the sample image into a model to be trained to obtain an image labeled by the model; analyzing the image marked by the model and the marked sample image to determine the loss value of the image marked by the model; comparing the loss value with a target value, and determining whether the model to be trained is trained according to a comparison result; and adjusting relevant parameters in the model to be trained in response to the fact that the model to be trained is not trained completely.

According to one or more embodiments of the present disclosure, the step of obtaining the image segmentation network in the image segmentation unit further includes: and determining the model to be trained as the image segmentation network in response to determining that the training of the model to be trained is completed.

According to one or more embodiments of the present disclosure, there is provided an electronic device including: one or more processors; a storage device having one or more programs stored thereon which, when executed by one or more processors, cause the one or more processors to implement a method as in any above.

According to one or more embodiments of the present disclosure, a computer-readable medium is provided, on which a computer program is stored, wherein the program, when executed by a processor, implements the method as any one of the above.

The foregoing description is only exemplary of the preferred embodiments of the disclosure and is illustrative of the principles of the technology employed. It will be appreciated by those skilled in the art that the scope of the invention in the embodiments of the present disclosure is not limited to the specific combination of the above-mentioned features, but also encompasses other embodiments in which any combination of the above-mentioned features or their equivalents is made without departing from the inventive concept as defined above. For example, the above features and (but not limited to) technical features with similar functions disclosed in the embodiments of the present disclosure are mutually replaced to form the technical solution.

Claims

1. An image processing method comprising:

acquiring a target image and a mark of a user for a target object in the target image;

determining a first probability that each pixel point in the target image is a target pixel point based on the target image and the mark;

extracting a first image region corresponding to the target object based at least in part on the first probability.

2. The method of claim 1, wherein the extracting the first image region corresponding to the target object comprises:

performing image segmentation in a second image region containing the user mark to obtain a second probability that each pixel point in the second image region is a target pixel point;

extracting the first image region based on the first probability and the second probability, wherein the first image region is a set of target pixel points.

3. The method of claim 1, wherein after extracting the first image region corresponding to the target object, the method further comprises:

in response to determining that the size of the target object is less than a predetermined threshold, a first image region corresponding to the target object is presented in a magnified manner.

4. The method of claim 1, wherein said determining, based on the target image and the label, a first probability that each pixel in the target image is a target pixel comprises:

and determining a first probability that each pixel point in the target image is a target pixel point based on the target image and the mark by using a matting algorithm.

5. The method of claim 2, wherein the performing image segmentation in the second image region including the user tag to obtain a second probability that each pixel point in the second image region is a target pixel point comprises:

and inputting the second image area into an image segmentation network to obtain a second probability that each pixel point in the second image area is a target pixel point.

6. The method of claim 5, wherein the image segmentation network is obtained by:

obtaining a sample, wherein the sample comprises a sample image and an annotated sample image corresponding to the sample image;

inputting the sample image into a model to be trained to obtain an image labeled by the model;

analyzing the image labeled by the model and the labeled sample image to determine a loss value of the image labeled by the model;

comparing the loss value with a target value, and determining whether the model to be trained is trained according to a comparison result;

adjusting relevant parameters in the model to be trained in response to determining that the model to be trained is not trained.

7. The method of claim 6, wherein the steps further comprise:

in response to determining that the model to be trained is trained, determining the model to be trained as the image segmentation network.

8. An image processing apparatus comprising:

an acquisition unit configured to acquire a target image and a mark of a user for a target object in the target image;

a determining unit configured to determine a first probability that each pixel point in the target image is a target pixel point based on the target image and the label;

an extraction unit configured to extract a first image region corresponding to the target object based at least in part on the first probability.

9. An electronic device, comprising:

one or more processors;

a storage device having one or more programs stored thereon,

when executed by the one or more processors, cause the one or more processors to implement the method of any one of claims 1-7.

10. A non-transitory computer readable storage medium having stored thereon a computer program, wherein the program when executed by a processor implements the method of any one of claims 1-7.