WO2020063014A1 - Image display method and related product - Google Patents

Abstract

Disclosed by the embodiments of the present application are an image display method and related product, wherein the method comprises: obtaining a historical operation parameter set of each image in a plurality of images to obtain a plurality of historical operation parameter sets; according to the plurality of historical operation parameter sets, conducting a preference degree evaluation on each image in the plurality of images to obtain a plurality of evaluation values, each image corresponding to an evaluation value, and the preference degree being used to represent a preference degree of a target object for the image; according to a preset depth learning model, classifying the plurality of images to obtain a plurality of categories of images, each category of images comprising at least one image; determining an evaluation value corresponding to each category of images in the plurality of categories of images to obtain a plurality of evaluation value sets, each category of images corresponding to an evaluation value set; according to the plurality of evaluation value sets, determining a preference degree corresponding to each category of images to obtain a plurality of preference degrees; and displaying the plurality of categories of images according to the plurality of preference degrees. Employing the embodiments of the present application may implement a photo album display function according to preference degrees.

Description

Image display method and related products Technical field

The present application relates to the technical field of electronic devices, and in particular, to an image display method and related products.

Background technique

With the widespread application of electronic devices (such as mobile phones, tablet computers, etc.), electronic devices can support more and more applications, and their functions are becoming more and more powerful. Electronic devices can not only take pictures, but also implement various PS (Photoshop) functions. .

At present, electronic devices cannot know the degree of user preference for images, and therefore, they cannot accurately display images.

Summary of the Invention

The embodiments of the present application provide an image display method and related products, which can accurately display images, and improve the intelligence and convenience of electronic devices.

In a first aspect, an embodiment of the present application provides an image display method, including:

Acquiring the historical operation parameter set of each of the multiple images to obtain multiple historical operation parameter sets, each historical operation parameter set including at least one operation parameter, each operation parameter used to describe an operation action on the image;

Evaluate each image in the multiple images according to the multiple historical operation parameter sets to obtain multiple evaluation values. Each image corresponds to an evaluation value. The preference degree is used to indicate the degree of preference of the target object to the image. ;

Classifying the multiple images according to a preset deep learning model to obtain multiple types of images, each type of image including at least one image;

Determining an evaluation value corresponding to each type of images in the multiple types of images, obtaining multiple evaluation value sets, and each type of image corresponding to one evaluation value set;

Determining a preference level corresponding to each type of the multiple types of images according to the multiple evaluation value sets, and obtaining multiple preference levels;

The multiple types of images are displayed according to the multiple preference levels.

In a second aspect, an embodiment of the present application provides a picture image display device, where the device includes:

An obtaining unit is configured to obtain a historical operation parameter set of each of multiple images, and obtain multiple historical operation parameter sets. Each historical operation parameter set includes at least one operation parameter. Each operation parameter is used to express an image of the image. An action

An evaluation unit, configured to evaluate the preference of each of the multiple images according to the multiple historical operation parameter sets to obtain multiple evaluation values, each of which corresponds to an evaluation value, and the preference is used to represent a target object Degree of preference for images;

A classification unit, configured to classify the multiple images according to a preset deep learning model to obtain multiple types of images, each type of images including at least one image;

A determining unit, configured to determine an evaluation value corresponding to each type of images in the multiple types of images, to obtain multiple evaluation value sets, and each type of image corresponds to an evaluation value set; and determine the multiple values according to the multiple evaluation value sets The preference level corresponding to each type of image in the class image, to obtain multiple preference levels;

A display unit is configured to display the multiple types of images according to the multiple preference levels.

According to a third aspect, an embodiment of the present application provides an electronic device including a processor, a memory, a communication interface, and one or more programs, wherein the one or more programs are stored in the memory and configured to be processed by the foregoing. The program executes, and the program includes instructions for performing steps in the first aspect of the embodiment of the present application.

According to a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, where the computer-readable storage medium stores a computer program for electronic data exchange, and the computer program enables a computer to execute a computer program as described in the first embodiment of the present application. Some or all of the steps described in one aspect.

In a fifth aspect, an embodiment of the present application provides a computer program product, wherein the computer program product includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to cause a computer to execute the implementation of the present application. Some or all of the steps described in the first aspect of the example. The computer program product can be a software installation package.

BRIEF DESCRIPTION OF THE DRAWINGS

In order to explain the technical solutions in the embodiments of the present application or the prior art more clearly, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings in the following description are merely These are some embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained according to these drawings without paying creative labor.

FIG. 1A is a schematic structural diagram of an electronic device according to an embodiment of the present application; FIG.

FIG. 1B is a schematic flowchart of an image display method according to an embodiment of the present application; FIG.

2 is a schematic flowchart of another image display method according to an embodiment of the present application;

3 is a schematic structural diagram of an electronic device according to an embodiment of the present application;

4A is a functional block diagram of an image display device according to an embodiment of the present application;

FIG. 4B is a block diagram of functional units of another image display device according to an embodiment of the present application.

detailed description

In order to enable those skilled in the art to better understand the solutions of the present application, the technical solutions in the embodiments of the present application will be described clearly and completely in combination with the drawings in the embodiments of the present application. Obviously, the described embodiments are only These are part of the embodiments of the present application, but not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by a person of ordinary skill in the art without creative efforts shall fall within the protection scope of the present application.

The terms “first”, “second”, and the like in the description and claims of the present application and the above-mentioned drawings are used to distinguish different objects, and are not used to describe a specific order. Furthermore, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device containing a series of steps or units is not limited to the listed steps or units, but optionally also includes steps or units that are not listed, or optionally also includes Other steps or units inherent to these processes, methods, products or equipment.

Reference to "an embodiment" herein means that a particular feature, structure, or characteristic described in connection with the embodiment may be included in at least one embodiment of the present application. The appearances of this phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are they independent or alternative embodiments that are mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

The electronic devices involved in the embodiments of the present application may include various handheld devices with wireless communication functions, vehicle-mounted devices, wearable devices (smart watches, smart bracelets, wireless headsets, augmented reality / virtual reality devices, smart glasses), Computing equipment or other processing equipment connected to a wireless modem, as well as various forms of user equipment (UE), mobile stations (MS), terminal devices, and so on. For ease of description, the devices mentioned above are collectively referred to as electronic devices.

The embodiments of the present application are described in detail below.

Please refer to FIG. 1A, which is a schematic structural diagram of an electronic device according to an embodiment of the present application. The electronic device includes a control circuit and an input-output circuit, and the input-output circuit is connected to the control circuit.

The control circuit may include a storage and processing circuit. The storage circuit in the storage and processing circuit may be a memory, such as a hard disk drive memory, a non-volatile memory (such as a flash memory or other electronic programmable read-only memory used to form a solid-state drive, etc.), a volatile memory (such as a static Or dynamic random access memory, etc.), this embodiment is not limited. The processing circuit in the storage and processing circuit can be used to control the operation of the electronic device. The processing circuit may be based on one or more microprocessors, microcontrollers, digital signal processors, baseband processors, power management units, audio codec chips, application specific integrated circuits, display driver integrated circuits, or quantum chips. Nanochips, etc.

The storage and processing circuit can be used to run software in an electronic device, such as playing an incoming call alert ringing application, playing a short message alert ringing application, playing an alarm alert ringing application, playing a media file application, Internet protocol voice (voice Over Internet Protocol (VOIP) telephone calling applications, operating system functions, etc. These software can be used to perform some control operations, such as playing the call alert ring, playing the short message alert ring, playing the alarm alert ring, playing media files, making voice phone calls, and other functions in electronic devices. The examples are not limited.

Among them, the input-output circuit can be used to enable the electronic device to implement data input and output, that is, to allow the electronic device to receive data from an external device and to allow the electronic device to output data from the electronic device to the external device.

The input-output circuit may further include a sensor. The sensor may include an ambient light sensor, an infrared proximity sensor based on light and capacitance, a fingerprint sensor, an ultrasonic sensor, and a touch sensor (for example, a light touch sensor and / or a capacitive touch sensor, where the touch sensor may be a touch display screen) One part can also be used independently as a touch sensor structure), acceleration sensor, gravity sensor, and other sensors. Fingerprint sensors are at least one of the following: capacitive fingerprint sensors, ultrasonic fingerprint sensors, optical fingerprint sensors, etc., which are not limited here. . The input-output circuit may further include an audio component, and the audio component may be used to provide audio input and output functions for the electronic device. The audio component may also include a tone generator and other components for generating and detecting sound.

The input-output circuit may further include a plurality of display screens. The above multiple display screens include two or more display screens, and multiple display screens can be set separately from the front and back of the electronic device. Of course, in practical applications, multiple display screens can also be set at other positions. It may include one or a combination of a liquid crystal display, an organic light emitting diode display, an electronic ink display, a plasma display, and a display using other display technologies. The display screen may include a touch sensor array (ie, the display screen may be a touch display screen). The touch sensor can be a capacitive touch sensor formed by a transparent array of touch sensor electrodes (such as indium tin oxide (ITO) electrodes), or it can be a touch sensor formed using other touch technologies, such as sonic touch, pressure-sensitive touch, resistance Touch, optical touch, etc. are not limited in the embodiments of the present application.

The input-output circuit can further include a communication circuit that can be used to provide the electronic device with the ability to communicate with external devices. The communication circuit may include analog and digital input-output interface circuits, and wireless communication circuits based on radio frequency signals and / or optical signals. The wireless communication circuit in the communication circuit may include a radio frequency transceiver circuit, a power amplifier circuit, a low noise amplifier, a switch, a filter, and an antenna. For example, the wireless communication circuit in the communication circuit may include a circuit for supporting near field communication (NFC) by transmitting and receiving a near field coupled electromagnetic signal. For example, the communication circuit may include a near field communication antenna and a near field communication transceiver. The communication circuit may also include a cellular phone transceiver and antenna, a wireless local area network transceiver circuit and antenna, and the like.

The input-output circuit may further include other input-output units. The input-output unit may include a button, a joystick, a click wheel, a scroll wheel, a touch pad, a keypad, a keyboard, a camera, a light emitting diode, and other status indicators.

The electronic device may further include a battery (not shown), and the battery is used to provide power to the electronic device.

Please refer to FIG. 1B. FIG. 1B is a schematic flowchart of an image display method provided by an embodiment of the present application. As shown in the figure, it is applied to the electronic device shown in FIG. 1A. The image display method includes:

101. Obtain a historical operation parameter set of each of multiple images, and obtain multiple historical operation parameter sets. Each historical operation parameter set includes at least one operation parameter. Each operation parameter is used to describe an operation action on the image. .

Among them, the electronic device may store multiple images in advance. The user will perform different operations on each image, for example, opening the image, closing the image, deleting the image, restoring the image, reposting the image, beautifying the image, collecting the image, etc., which are not limited here, so different operations , You can generate different operating parameters. For example, when you open an image, you can record the duration of the open image. For another example, you can record the operating parameter as 1, for favorite images, and you can record the operating parameter as 0 if you do not favorite images. Each image can correspond to a historical operation parameter set, multiple images can correspond to multiple historical operation parameter sets, each historical operation parameter set can correspond to one operation parameter, and each operation parameter is used to express an operation on the image An operation action. Of course, the historical operation parameter set for each image can be saved in the image operation history database.

Optionally, each of the above historical operation parameter sets may include at least one operation parameter, and the operation parameter may be at least one of the following: the average dwell time of the image (that is, the average time for the user to display the image), whether to save the operation, and whether to forward the operation , The number of deletions, whether to restore the operation, whether the puzzle operation, etc., are not limited here.

Optionally, in the above step 101, the historical operation parameter set of each of the multiple images is acquired, and multiple historical operation parameter sets are obtained, which may be implemented as follows:

Obtain the historical operation parameter set of each of the multiple images in a preset time period to obtain multiple historical operation parameter sets.

The preset time period can be set by the user or defaulted by the system. For different time periods, the corresponding historical operation parameter sets are different. For example, in real life, a user may be interested in an image for a period of time, and may not be interested in the image for another period of time. In this case, a reasonable choice can be made. The operation of the time period that the user is interested in.

102. Evaluate each image in the multiple images according to the multiple historical operation parameter sets to obtain multiple evaluation values. Each image corresponds to an evaluation value. The preference degree is used to indicate a target object's Degree of preference.

Among them, each historical operation parameter set includes at least one operation parameter, and furthermore, an image can be evaluated for preference based on these operation parameters. Generally, users will largely like the image multiple times. Open, or even favorite, for the images you don't like, it is opened less often. Therefore, each image in multiple images can be evaluated based on multiple historical operation parameter sets to obtain multiple evaluation values. Each image corresponds to an evaluation value. The preference degree is used to indicate the degree of preference of the target object to the image. The target object may be the user or the owner of the electronic device.

Wherein, in the above step 102, a preference degree evaluation is performed on each of the multiple images according to the multiple historical operation parameter sets to obtain multiple evaluation values, which may include the following steps:

21. Obtain a weight corresponding to each operation parameter in the historical operation parameter set i to obtain at least one weight, and the historical operation parameter set i is any one of the multiple historical operation parameter sets;

22. Perform a weighted operation according to the operation parameter in the historical operation parameter set i and the at least one weight to obtain an evaluation value.

The historical operation parameter set i is taken as an example. The historical operation parameter set i is any historical operation parameter set of multiple historical operation parameter sets. Each historical operation parameter set includes at least one operation parameter, and each operation parameter corresponds to at least one. The weight value, the mapping relationship between the operation parameter and the weight value can be stored in the electronic device in advance, and further, the weight value corresponding to each operation parameter can be determined according to the mapping relationship. Assume that the historical operation parameter set i includes n operation parameters, each of which corresponds to a weight, and each weight can be between 0 and 1, as shown below:

Operating parameters	Weight
Operation parameter 1	Weight 1
Operation parameter 2	Weight 2
...	...
Operating parameter n	Weight n

Furthermore, a weighting operation may be performed according to the operation parameter in the historical operation parameter i and at least one weight value, and the evaluation value = operation parameter 1 * weight value 1 + operation parameter 2 * weight value 2 + ... + operation parameter n * weight value n In this way, the evaluation value corresponding to each historical operation parameter set can be realized.

103. Classify the multiple images according to a preset deep learning model to obtain multiple types of images, each type of images including at least one image.

The preset deep learning model may be at least one of the following: a convolutional neural network deep learning model, an Adaboost deep learning model, and a random forest deep learning model, which are not repeated here. In specific implementation, the electronic device may collect data for each The positive sample image and negative sample image of a class of images are trained to obtain a preset deep learning model. Specifically, the electronic device may classify multiple images according to a preset deep learning model to obtain multiple types of images. Each type of image includes at least one image, and each type of image may correspond to a category identifier. The category identifier may be at least one of the following: night portrait, beach, snow, baby, pet, which is not limited herein.

Optionally, the above steps 102 and 103 may be performed in parallel, or step 103 may be performed before step 102, which is not limited herein.

104. Determine an evaluation value corresponding to each type of images in the multiple types of images to obtain multiple evaluation value sets, and each type of image corresponds to an evaluation value set.

Each type of image includes at least one image, and each image corresponds to an evaluation value. Furthermore, each type of image may correspond to an evaluation value set, and multiple types of images may correspond to multiple evaluation value sets.

105. Determine a preference level corresponding to each type of images in the multiple types of images according to the multiple evaluation value sets, and obtain multiple preference levels.

Among them, the evaluation value reflects the user's preference for a single image to a certain extent, and the evaluation value set reflects the user's preference for a type of images to a certain extent. Therefore, multiple types of images can be determined based on multiple evaluation value sets. Multiple preference levels are obtained for the preference levels corresponding to each type of image. For example, the preference level corresponding to each type of image can be determined according to the average evaluation value in multiple evaluation value sets, that is, the mapping between the average evaluation value and the preference level is stored in advance. The relationship can further determine the preference level corresponding to each average evaluation value. For example, the larger the average evaluation value, the higher the preference level. For example, the correspondence between each type of image can be determined according to the sum of the evaluation values in multiple evaluation value sets. The preference level of, that is, the mapping relationship between the evaluation value and the preference level is stored in advance, and then the preference level corresponding to the sum of each evaluation value can be determined. For example, the larger the sum of the evaluation values, the higher the preference level.

Optionally, in step 105, the preference level corresponding to each type of images in the multiple types of images is determined according to the multiple evaluation value sets, and multiple preference levels are obtained, which may include the following steps:

51. Determine the number of evaluation values and the average value of the evaluation values in each of the plurality of evaluation value sets, respectively, to obtain multiple numbers of evaluation values and multiple average values.

52. Obtain a first weight corresponding to the number of evaluation values in each of the multiple evaluation value sets and a second weight corresponding to the average value of the evaluation values;

53. Perform weighting operations according to the number of evaluation values corresponding to each evaluation value set, the average value of the evaluation values, the first weight value, and the second weight value to obtain a plurality of preference values;

54. According to a preset mapping relationship between preference values and preference levels, determine a preference level corresponding to each preference value in the plurality of preference values to obtain a plurality of preference levels.

Among them, this application considers that there may be a large number of images of some categories, but the average evaluation value is low, or the total evaluation value is low. Therefore, the user ’s preference for a certain type of image is combined with the two dimensions of the evaluation value and the average evaluation value. Perform comprehensive evaluation. In specific implementation, the electronic device can determine the number of evaluation values and the average value of the evaluation values corresponding to each evaluation value set in multiple evaluation value sets, and obtain multiple evaluation value numbers and multiple average evaluation values. The mapping relationship between the number of evaluation values and the first weight is stored, and further, according to the mapping relationship, the first weight corresponding to the number of evaluation values in each evaluation value set of multiple evaluation value sets can be determined, and the average value and The mapping relationship between the second weight values, and further, according to the mapping relationship, a second weight value corresponding to each evaluation value average in multiple evaluation value sets is determined, and further, the corresponding The number of evaluation values, the average value of the evaluation values, the first weight value and the second weight value are weighted to obtain a plurality of preference values. Specifically, For any evaluation value set, its corresponding preference value = number of evaluation values * first weight value + average evaluation value * second weight value. The electronic device can also store in advance the preset preference value and the preference level. The mapping relationship is such that, according to the mapping relationship, a preference level corresponding to each preference value in the multiple preference values is determined, and multiple preference levels are obtained. For example, the preference level may be: best, like, or ordinary, which is not limited herein.

106. Display the multiple types of images according to the multiple preference levels.

Among them, the different preference levels explain the user's preference for such images to a certain extent. Therefore, in a specific implementation, the higher the preference level, the higher the priority can be given to displaying the image corresponding to the preference level.

Optionally, the above step 106, displaying the multiple types of images according to the multiple preference levels, may include the following steps:

61. Select at least one target image with the largest evaluation value from each of the multiple types of images;

62. Use the at least one target image as a cover image to obtain multiple cover images, each type of image corresponding to a cover image;

63. Determine the display order of the multiple types of images according to the multiple preference levels;

64. Display the plurality of cover images according to the display order.

Among them, multiple preference levels can be used to form a priority order, and further, the display order of multiple types of images is determined according to the multiple preference levels. For example, the higher the preference level, the higher the priority display. Each of the multiple types of images in the electronic device corresponds to an evaluation value. Therefore, the target image corresponding to the maximum evaluation value in each type of image can be obtained. Of course, it is also possible that each type of image includes multiple Multiple target images corresponding to the maximum evaluation value. Furthermore, at least one target image can be used as a cover image, each type of image corresponds to a cover image, and multiple target images can be used as a cover image in the form of a puzzle. Finally, according to The display sequence displays multiple cover images, and each cover image corresponds to a type of image.

Optionally, after step 64 above, the method may further include the following steps:

A1. Receive a click instruction for a cover image j, where the cover image j is a cover image of any of the multiple types of images;

A2: Display all images in a type of image in the cover image j.

The cover image j is taken as an example. The cover image j is a cover image of any type of images. In specific implementation, a click instruction for the cover image j can be received to display a type of image in the cover image j All the images in the image, that is, when the user views any type of image, the user can click on the cover image corresponding to the type of image to display all the images in the type of image, so that the user's viewing efficiency can be improved.

Optionally, before step 101, the following steps may be further included:

B1. Obtain image library;

B2. Obtaining a preset identification set;

B3. Filter the image library according to the preset identification set to obtain the multiple images.

The preset identification set can be set by the user or defaulted by the system. The preset identification set may be at least one of the following: a preset time period, a preset storage path, a preset format type, a preset photographing mode, a preset camera, a preset photographing location, and the like, which are not limited herein. Specifically, the electronic device can obtain an image library. Of course, the image library can contain a large number of images, and further, these images can be filtered, that is, a preset identification set can be obtained, and the image library can be filtered according to the preset identification set. To get multiple images.

For example, the electronic device can set a preset identification set, such as: image file type whitelist (specified image format), such as retaining jpg format, bmp format, png format, or set a whitelist (specified path) where the image is located, such as Only the / storage / 0 / DCIM directory is retained, and further, an image operation history database is established, and the open function and the close function in the system libc are modified, and each time a file is opened, it is determined whether the opened image is an image of a specified image format in a specified path. If yes, record the open file name, path information, and opening time into the image operation history database, record the image closing time point into the image operation history database when the image is closed, and use a preset deep learning model pair The images in the database are classified, for example, the category identifier can be divided into: night portraits, beaches, snow, babies, pets, etc., and further, according to the operation data in the image operation history database (such as the number of times the image was opened, the image Average dwell time) and classification results to count the number of images opened and various types of images , Of course, you can also calculate the time interval between the opening time and the creation time of each image based on the data in the database, and normalize the residence time and time interval to a value range of 1 to 5, for each of the albums. The classification of each image is classified into categories, and the time interval between the image and the moment of creation is calculated, and the category score of the image is calculated. Specifically, the number of openings of each category and the average dwell time can be added to each The category score of the image is weighted and summed with the time interval from the moment of creation. The resulting value is used as the user's potential preference value for the image. The image is used to determine the preference level corresponding to each type of image in the album according to the preference value. Class image.

It can be seen that the image display method described in the embodiment of the present application is applied to an electronic device to obtain a historical operation parameter set of each of multiple images, to obtain multiple historical operation parameter sets, and each historical operation parameter set. Including at least one operation parameter, each operation parameter is used to express an operation action on an image, and each image in a plurality of images is evaluated for preference according to multiple historical operation parameter sets to obtain multiple evaluation values, each image corresponding to An evaluation value, the degree of preference is used to indicate the degree of preference of the target object for the image, and multiple images are classified according to a preset deep learning model to obtain multiple types of images. Each type of images includes at least one image. The evaluation value corresponding to the class image is obtained by multiple evaluation value sets, and each class image corresponds to an evaluation value set. Based on the multiple evaluation value sets, the preference level corresponding to each type of image in the multi-type image is determined, and multiple preference levels are obtained. Multiple preference levels are used to display multiple types of images, so that the degree of preference for each type of image can be determined, and then, Album display function, improved intelligence and convenience of electronic equipment.

Consistent with the embodiment shown in FIG. 1B described above, please refer to FIG. 2. FIG. 2 is a schematic flowchart of an image display method provided by an embodiment of the present application. As shown in the figure, it is applied to the electronic device shown in FIG. 1A. This image display method includes:

201. Obtain an image library.

202. Obtain a preset identification set.

203: Filter the image library according to the preset identification set to obtain multiple images.

204. Obtain a historical operation parameter set of each of the multiple images, and obtain multiple historical operation parameter sets. Each historical operation parameter set includes at least one operation parameter. Each operation parameter is used to express a Operation action.

205: Perform a preference evaluation on each of the multiple images according to the multiple historical operation parameter sets to obtain multiple evaluation values. Each image corresponds to an evaluation value. The preference is used to indicate a target object's Degree of preference.

206: Classify the multiple images according to a preset deep learning model to obtain multiple types of images, each type of images including at least one image.

207. Determine an evaluation value corresponding to each type of images in the multiple types of images to obtain multiple evaluation value sets, and each type of image corresponds to one evaluation value set.

208. Determine a preference level corresponding to each type of images in the multiple types of images according to the multiple evaluation value sets, and obtain multiple preference levels.

209. Display the multiple types of images according to the multiple preference levels.

For the detailed description of the above steps 201 to 209, reference may be made to the corresponding steps of the image display method described in FIG. 1B described above, and details are not described herein again.

It can be seen that the image display method described in the embodiment of the present application is applied to an electronic device, obtains an image library, obtains a preset identification set, filters the image library according to the preset identification set, obtains multiple images, and obtains the multiple images. The historical operation parameter set of each image in each image is obtained from multiple historical operation parameter sets. Each historical operation parameter set includes at least one operation parameter. Each operation parameter is used to describe an operation action on the image. The historical operation parameter set evaluates each image in multiple images to obtain multiple evaluation values. Each image corresponds to an evaluation value. The preference is used to indicate the degree of preference of the target object to the image. According to a preset deep learning model Classify multiple images to obtain multiple types of images. Each type of image includes at least one image. Determine the evaluation value corresponding to each type of image in the multiple types of images to obtain multiple evaluation value sets. Each type of image corresponds to an evaluation value set. , Determine the preference level corresponding to each type of image in multiple types of images based on multiple evaluation value sets, obtain multiple preference levels, and The preference level displays multiple types of images, so that the preference level of each type of image can be determined, and further, the album display function is realized according to the preference level, which improves the intelligence and convenience of the electronic device.

Consistent with the above embodiments, please refer to FIG. 3, which is a schematic structural diagram of an electronic device according to an embodiment of the present application. As shown in the figure, the electronic device includes a processor, a memory, a communication interface, and one or more A program, wherein the one or more programs are stored in the memory and configured to be executed by the processor. In the embodiment of the present application, the electronic device is in a screen-off state, and the program includes instructions for performing the following steps:

Acquiring the historical operation parameter set of each of the multiple images to obtain multiple historical operation parameter sets, each historical operation parameter set including at least one operation parameter, each operation parameter used to describe an operation action on the image;

Evaluate each image in the multiple images according to the multiple historical operation parameter sets to obtain multiple evaluation values. Each image corresponds to an evaluation value. The preference degree is used to indicate the degree of preference of the target object to the image. ;

Classifying the multiple images according to a preset deep learning model to obtain multiple types of images, each type of image including at least one image;

Determining an evaluation value corresponding to each type of images in the multiple types of images, obtaining multiple evaluation value sets, and each type of image corresponding to one evaluation value set;

Determining a preference level corresponding to each type of the multiple types of images according to the multiple evaluation value sets, and obtaining multiple preference levels;

The multiple types of images are displayed according to the multiple preference levels.

It can be seen that the electronic device described in the embodiment of the present application obtains the historical operation parameter set of each of the multiple images to obtain multiple historical operation parameter sets. Each historical operation parameter set includes at least one operation parameter. Each operation parameter is used to express an operation action on an image. Each image in multiple images is evaluated for preference based on multiple historical operation parameter sets to obtain multiple evaluation values. Each image corresponds to an evaluation value. It is used to indicate the degree of preference of the target object to the image. Multiple images are classified according to a preset deep learning model to obtain multiple types of images. Each type of image includes at least one image. The evaluation value corresponding to each type of images in the multiple types of images is determined To obtain multiple evaluation value sets, each type of image corresponds to an evaluation value set, and according to the multiple evaluation value sets, determine the preference level corresponding to each type of image in the multiple types of images, obtain multiple preference levels, and display multiple according to multiple preference levels. Class images, so that the degree of preference of each type of image can be determined, and further, the album display function can be implemented according to the degree of preference to improve Smart and convenience of electronic equipment.

In a possible example, in terms of acquiring historical operation parameter sets of each of the multiple images and obtaining multiple historical operation parameter sets, the foregoing program includes instructions for performing the following steps:

A historical operation parameter set of each of the multiple images in a preset time period is acquired, and multiple historical operation parameter sets are obtained.

In a possible example, the operation parameter is at least one of the following: the average dwell time of the image (that is, the average time for the user to display the image), whether to bookmark the operation, whether to forward the operation, delete times, whether to restore the operation, and whether to puzzle operating.

In a possible example, in terms of performing preference evaluation on each of the multiple images according to the multiple historical operation parameter sets to obtain multiple evaluation values, the foregoing program includes a method for performing the following steps: instruction:

Acquiring a weight corresponding to each operation parameter in the historical operation parameter set i to obtain at least one weight, the historical operation parameter set i being any one of the historical operation parameter sets of the multiple historical operation parameter sets;

A weighting operation is performed according to an operation parameter in the historical operation parameter set i and the at least one weight value to obtain an evaluation value.

In one possible example, the above program also includes instructions for performing the following steps:

Get image library;

Obtaining a preset identification set;

The image library is filtered according to the preset identification set to obtain the multiple images.

In a possible example, the preset identification set includes at least one of the following: a preset time period, a preset storage path, a preset format type, a preset photographing mode, a preset camera, and a preset photograph location.

In a possible example, in terms of determining a preference level corresponding to each type of images in the multiple types of images according to the multiple evaluation value sets, and obtaining multiple preference levels, the foregoing program includes instructions for performing the following steps: :

Respectively determining the number of evaluation values and the average value of the evaluation values in each of the plurality of evaluation value sets to obtain multiple evaluation value numbers and multiple evaluation value averages;

Acquiring a first weight corresponding to the number of evaluation values in each evaluation value set and a second weight corresponding to the average value of the evaluation values;

Perform weighting operations according to the number of evaluation values corresponding to each evaluation value set, the average value of the evaluation values, the first weight value, and the second weight value to obtain a plurality of preference values;

According to a preset mapping relationship between preference values and preference levels, a preference level corresponding to each preference value in the plurality of preference values is determined, and multiple preference levels are obtained.

In a possible example, in terms of displaying the multiple types of images according to the multiple preference levels, the foregoing program includes instructions for performing the following steps:

Selecting at least one target image with the largest evaluation value from each of the multiple types of images;

Using the at least one target image as a cover image to obtain multiple cover images, each type of image corresponding to a cover image;

Determining a display order of the multiple types of images according to the multiple preference levels;

The plurality of cover images are displayed according to the display order.

In one possible example, the above program also includes instructions for performing the following steps:

Receiving a click instruction for a cover image j, where the cover image j is a cover image of any one of the multiple types of images;

All images in a type of image in the cover image j are displayed.

The above mainly introduces the solution of the embodiment of the present application from the perspective of the method-side execution process. It can be understood that, in order to realize the above functions, the electronic device includes a hardware structure and / or a software module corresponding to each function. Those skilled in the art should easily realize that, with reference to the units and algorithm steps of the examples described in the embodiments provided herein, this application can be implemented in hardware or a combination of hardware and computer software. Whether a certain function is performed by hardware or computer software-driven hardware depends on the specific application of the technical solution and design constraints. Professional technicians may use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

The embodiments of the present application may divide the functional units of the electronic device according to the foregoing method examples. For example, each functional unit may be divided corresponding to each function, or two or more functions may be integrated into one processing unit. The above integrated unit may be implemented in the form of hardware or in the form of software functional unit. It should be noted that the division of the units in the embodiments of the present application is schematic, and is only a logical function division. There may be another division manner in actual implementation.

FIG. 4A is a block diagram of the functional units of the image display device 400 according to the embodiment of the present application. The image display device 400 is applied to an electronic device. The device 400 includes: an obtaining unit 401, an evaluation unit 402, a classification unit 403, a determination unit 404, and a display unit 405.

The obtaining unit 401 is configured to obtain a historical operation parameter set of each of multiple images, and obtain multiple historical operation parameter sets. Each historical operation parameter set includes at least one operation parameter, and each operation parameter is used to describe an image. An operation action of

An evaluation unit 402, configured to evaluate each image in the multiple images according to the multiple historical operation parameter sets to obtain multiple evaluation values, each image corresponds to an evaluation value, and the preference is used to indicate a target The object's preference for the image;

A classification unit 403, configured to classify the multiple images according to a preset deep learning model to obtain multiple types of images, each type of images including at least one image;

A determining unit 404, configured to determine an evaluation value corresponding to each type of images in the multiple types of images, to obtain multiple evaluation value sets, each type of image corresponding to an evaluation value set; and determine the Preference levels corresponding to each type of images in multiple types of images, to obtain multiple preference levels;

The display unit 405 is configured to display the multiple types of images according to the multiple preference levels.

It can be seen that the image display device described in the embodiment of the present application is applied to an electronic device to obtain a historical operation parameter set of each of multiple images, to obtain multiple historical operation parameter sets, and each historical operation parameter set. Including at least one operation parameter, each operation parameter is used to express an operation action on an image, and each image in a plurality of images is evaluated for preference according to multiple historical operation parameter sets to obtain multiple evaluation values, each image corresponding to An evaluation value, the degree of preference is used to indicate the degree of preference of the target object for the image, and multiple images are classified according to a preset deep learning model to obtain multiple types of images. Each type of images includes at least one image. The evaluation value corresponding to the class image is obtained by multiple evaluation value sets, and each class image corresponds to an evaluation value set. Based on the multiple evaluation value sets, the preference level corresponding to each type of image in the multi-type image is determined, and multiple preference levels are obtained. Multiple preference levels are used to display multiple types of images, so that the degree of preference for each type of image can be determined, and then, Album display function, improved intelligence and convenience of electronic equipment.

In a possible example, in terms of acquiring the historical operation parameter set of each of the multiple images and obtaining multiple historical operation parameter sets, the acquiring unit 401 is specifically configured to:

A historical operation parameter set of each of the multiple images in a preset time period is acquired, and multiple historical operation parameter sets are obtained.

In a possible example, the operation parameter is at least one of the following: the average dwell time of the image (that is, the average time for the user to display the image), whether to bookmark the operation, whether to forward the operation, delete times, whether to restore the operation, and whether to puzzle operating.

In a possible example, in terms of performing a preference evaluation on each of the multiple images according to the multiple historical operation parameter sets to obtain multiple evaluation values, the evaluation unit 402 is specifically configured to:

Acquiring a weight corresponding to each operation parameter in the historical operation parameter set i to obtain at least one weight, the historical operation parameter set i being any one of the historical operation parameter sets of the multiple historical operation parameter sets;

A weighting operation is performed according to an operation parameter in the historical operation parameter set i and the at least one weight value to obtain an evaluation value.

In a possible example, as shown in FIG. 4B, FIG. 4B is still another modified structure of the image display device shown in FIG. 4A. Compared with FIG. 4A, it may further include a screening unit 406, which is as follows:

The obtaining unit 401 is further specifically configured to obtain an image library; and obtain a preset identifier set;

The filtering unit 406 is configured to filter the image library according to the preset identification set to obtain the multiple images.

In a possible example, the preset identification set includes at least one of the following: a preset time period, a preset storage path, a preset format type, a preset photographing mode, a preset camera, and a preset photograph location.

In a possible example, in determining the preference level corresponding to each type of images in the multiple types of images according to the multiple evaluation value sets, and obtaining multiple preference levels, the determining unit 404 is specifically configured to:

Respectively determining the number of evaluation values and the average value of the evaluation values in each of the plurality of evaluation value sets to obtain multiple evaluation value numbers and multiple evaluation value averages;

Acquiring a first weight corresponding to the number of evaluation values in each evaluation value set and a second weight corresponding to the average value of the evaluation values;

Perform weighting operations according to the number of evaluation values corresponding to each evaluation value set, the average value of the evaluation values, the first weight value, and the second weight value to obtain a plurality of preference values;

According to a preset mapping relationship between preference values and preference levels, a preference level corresponding to each preference value in the plurality of preference values is determined, and multiple preference levels are obtained.

In a possible example, in terms of displaying the multiple types of images according to the multiple preference levels, the display unit 405 is specifically configured to:

Selecting at least one target image with the largest evaluation value from each of the multiple types of images;

Using the at least one target image as a cover image to obtain multiple cover images, each type of image corresponding to a cover image;

Determining a display order of the multiple types of images according to the multiple preference levels;

The plurality of cover images are displayed according to the display order.

In a possible example, the display unit 405 is further specifically configured to:

Receiving a click instruction for a cover image j, where the cover image j is a cover image of any one of the multiple types of images;

All images in a type of image in the cover image j are displayed.

An embodiment of the present application further provides a computer storage medium, wherein the computer storage medium stores a computer program for electronic data exchange, and the computer program causes a computer to perform a part or all of the steps of any method described in the foregoing method embodiments The computer includes electronic equipment.

An embodiment of the present application further provides a computer program product. The computer program product includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to cause a computer to execute any one of the methods described in the foregoing method embodiments. Part or all of the steps of a method. The computer program product may be a software installation package, and the computer includes an electronic device.

It should be noted that, for the foregoing method embodiments, for the sake of simple description, they are all described as a series of action combinations. However, those skilled in the art should know that this application is not limited by the described action order. Because according to the present application, certain steps may be performed in another order or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required for this application.

In the above embodiments, the description of each embodiment has its own emphasis. For a part that is not described in detail in one embodiment, reference may be made to related descriptions in other embodiments.

In the several embodiments provided in this application, it should be understood that the disclosed device may be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the above units is only a logical function division. In actual implementation, there may be another division manner. For example, multiple units or components may be combined or integrated. To another system, or some features can be ignored or not implemented. In addition, the displayed or discussed mutual coupling or direct coupling or communication connection may be indirect coupling or communication connection through some interfaces, devices or units, and may be electrical or other forms.

The units described above as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, which may be located in one place, or may be distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the objective of the solution of this embodiment.

In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each of the units may exist separately physically, or two or more units may be integrated into one unit. The above integrated unit may be implemented in the form of hardware or in the form of software functional unit.

When the above integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it may be stored in a computer-readable memory. Based on such an understanding, the technical solution of the present application essentially or part that contributes to the existing technology or all or part of the technical solution can be embodied in the form of a software product, which is stored in a memory, Several instructions are included to enable a computer device (which may be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the above method in each embodiment of the present application. The foregoing memories include: U disks, Read-Only Memory (ROM), Random Access Memory (RAM), mobile hard disks, magnetic disks, or optical disks and other media that can store program codes.

A person of ordinary skill in the art may understand that all or part of the steps in the various methods of the foregoing embodiments may be completed by a program instructing related hardware. The program may be stored in a computer-readable memory, and the memory may include a flash disk. , Read-only memory (English: Read-Only Memory, referred to as ROM), random access device (English: Random Access Memory, referred to as RAM), magnetic disks or optical disks, etc.

The embodiments of the present application have been described in detail above. Specific examples have been used in this document to explain the principles and implementation of the present application. The descriptions of the above embodiments are only used to help understand the methods and core ideas of the present application. Persons of ordinary skill in the art may change the specific implementation and application scope according to the idea of the present application. In summary, the content of this description should not be construed as a limitation on the present application.

Claims (20)

1. An image display method, comprising:

   Acquiring the historical operation parameter set of each of the multiple images to obtain multiple historical operation parameter sets, each historical operation parameter set including at least one operation parameter, each operation parameter used to describe an operation action on the image;

   Evaluate each image in the multiple images according to the multiple historical operation parameter sets to obtain multiple evaluation values. Each image corresponds to an evaluation value. The preference degree is used to indicate the degree of preference of the target object to the image. ;

   Classifying the multiple images according to a preset deep learning model to obtain multiple types of images, each type of image including at least one image;

   Determining an evaluation value corresponding to each type of images in the multiple types of images, obtaining multiple evaluation value sets, and each type of image corresponding to one evaluation value set;

   Determining a preference level corresponding to each type of the multiple types of images according to the multiple evaluation value sets, and obtaining multiple preference levels;

   The multiple types of images are displayed according to the multiple preference levels.
2. The method according to claim 1, wherein the acquiring the historical operation parameter set of each of the multiple images to obtain multiple historical operation parameter sets comprises:

   A historical operation parameter set of each of the multiple images in a preset time period is acquired, and multiple historical operation parameter sets are obtained.
3. The method according to claim 1 or 2, wherein the operation parameter is at least one of the following: the average dwell time of the image (that is, the average length of time the user displays the image), whether to save the operation, whether to forward the operation, or delete Number of times, whether to restore the operation, and whether to puzzle the operation.
4. The method according to any one of claims 1 to 3, wherein, according to the plurality of historical operation parameter sets, each of the plurality of images is subjected to a preference evaluation to obtain a plurality of evaluation values, include:

   Acquiring a weight corresponding to each operation parameter in the historical operation parameter set i to obtain at least one weight, the historical operation parameter set i being any one of the historical operation parameter sets of the multiple historical operation parameter sets;

   A weighting operation is performed according to an operation parameter in the historical operation parameter set i and the at least one weight value to obtain an evaluation value.
5. The method according to any one of claims 1-4, further comprising:

   Get image library;

   Obtaining a preset identification set;

   The image library is filtered according to the preset identification set to obtain the multiple images.
6. The method according to claim 5, wherein the preset identification set includes at least one of the following: a preset time period, a preset storage path, a preset format type, a preset photographing mode, and a preset Set up camera and preset photo location.
7. The method according to any one of claims 1-6, wherein the determining a preference level corresponding to each type of images in the multiple types of images according to the multiple evaluation value sets, and obtaining multiple preference levels, including :

   Respectively determining the number of evaluation values and the average value of the evaluation values in each of the plurality of evaluation value sets to obtain multiple evaluation value numbers and multiple evaluation value averages;

   Acquiring a first weight corresponding to the number of evaluation values in each evaluation value set and a second weight corresponding to the average value of the evaluation values;

   Perform weighting operations according to the number of evaluation values corresponding to each evaluation value set, the average value of the evaluation values, the first weight value, and the second weight value to obtain a plurality of preference values;

   According to a preset mapping relationship between preference values and preference levels, a preference level corresponding to each preference value in the plurality of preference values is determined, and multiple preference levels are obtained.
8. The method according to any one of claims 1 to 7, wherein the displaying the multiple types of images according to the multiple preference levels comprises:

   Selecting at least one target image with the largest evaluation value from each of the multiple types of images;

   Using the at least one target image as a cover image to obtain multiple cover images, each type of image corresponding to a cover image;

   Determining a display order of the multiple types of images according to the multiple preference levels;

   The plurality of cover images are displayed according to the display order.
9. The method according to claim 8, further comprising:

   Receiving a click instruction for a cover image j, where the cover image j is a cover image of any one of the multiple types of images;

   All images in a type of image in the cover image j are displayed.
10. An image display device, wherein the device includes:

    An obtaining unit is configured to obtain a historical operation parameter set of each of multiple images, and obtain multiple historical operation parameter sets. Each historical operation parameter set includes at least one operation parameter. Each operation parameter is used to express an image of the image. An action

    An evaluation unit, configured to evaluate the preference of each of the multiple images according to the multiple historical operation parameter sets to obtain multiple evaluation values, each of which corresponds to an evaluation value, and the preference is used to represent a target object Degree of preference for images;

    A classification unit, configured to classify the multiple images according to a preset deep learning model to obtain multiple types of images, each type of images including at least one image;

    A determining unit, configured to determine an evaluation value corresponding to each type of images in the multiple types of images, to obtain multiple evaluation value sets, and each type of image corresponds to an evaluation value set; and determine the multiple values according to the multiple evaluation value sets The preference level corresponding to each type of image in the class image, to obtain multiple preference levels;

    A display unit is configured to display the multiple types of images according to the multiple preference levels.
11. The apparatus according to claim 10, wherein in the step of obtaining historical operation parameter sets of each of the multiple images and obtaining multiple historical operation parameter sets, the acquiring unit is specifically configured to:

    A historical operation parameter set of each of the multiple images in a preset time period is acquired, and multiple historical operation parameter sets are obtained.
12. The device according to claim 10 or 11, wherein the operation parameter is at least one of the following: the average dwell time of the image (that is, the average time for the user to display the image), whether to save the operation, whether to forward the operation, or delete Number of times, whether to restore the operation, and whether to puzzle the operation.
13. The device according to any one of claims 10 to 12, wherein, in the method, a preference degree evaluation is performed on each of the multiple images according to the multiple historical operation parameter sets to obtain multiple evaluation values. In one aspect, the evaluation unit is specifically configured to:

    Acquiring a weight corresponding to each operation parameter in the historical operation parameter set i to obtain at least one weight, the historical operation parameter set i being any one of the historical operation parameter sets of the multiple historical operation parameter sets;

    A weighting operation is performed according to an operation parameter in the historical operation parameter set i and the at least one weight value to obtain an evaluation value.
14. The device according to any one of claims 10-13, wherein the device further comprises: a screening unit;

    The obtaining unit is further specifically configured to obtain an image library; and obtain a preset identifier set;

    The filtering unit is configured to filter the image library according to the preset identification set to obtain the multiple images.
15. The device according to claim 14, wherein the preset identification set includes at least one of the following: a preset time period, a preset storage path, a preset format type, a preset photographing mode, and a preset Set up camera and preset photo location.
16. The device according to any one of claims 10 to 15, wherein in determining the preference level corresponding to each type of images in the multiple types of images according to the multiple evaluation value sets, multiple preference levels are obtained. , The determining unit is specifically configured to:

    Respectively determining the number of evaluation values and the average value of the evaluation values in each of the plurality of evaluation value sets to obtain multiple evaluation value numbers and multiple evaluation value averages;

    Acquiring a first weight corresponding to the number of evaluation values in each evaluation value set and a second weight corresponding to the average value of the evaluation values;

    Perform weighting operations according to the number of evaluation values corresponding to each evaluation value set, the average value of the evaluation values, the first weight value, and the second weight value to obtain a plurality of preference values;

    According to a preset mapping relationship between preference values and preference levels, a preference level corresponding to each preference value in the plurality of preference values is determined, and multiple preference levels are obtained.
17. The device according to any one of claims 10 to 16, wherein the display unit is specifically configured to display the multiple types of images according to the multiple preference levels:

    Selecting at least one target image with the largest evaluation value from each of the multiple types of images;

    Using the at least one target image as a cover image to obtain multiple cover images, each type of image corresponding to a cover image;

    Determining a display order of the multiple types of images according to the multiple preference levels;

    The plurality of cover images are displayed according to the display order.
18. An electronic device, comprising a processor, a memory, a communication interface, and one or more programs. The one or more programs are stored in the memory and configured to be executed by the processor. The program includes instructions for performing the steps in the method according to any one of claims 1-9.
19. A computer-readable storage medium, characterized in that a computer program for electronic data interchange is stored, wherein the computer program causes a computer to execute the method according to any one of claims 1-9.
20. A computer program product, characterized in that the computer program product includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to cause a computer to execute the computer program according to any one of claims 1-9. The method described.

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