CN109508321B - Image display method and related product - Google Patents
Image display method and related product Download PDFInfo
- Publication number
- CN109508321B CN109508321B CN201811161360.4A CN201811161360A CN109508321B CN 109508321 B CN109508321 B CN 109508321B CN 201811161360 A CN201811161360 A CN 201811161360A CN 109508321 B CN109508321 B CN 109508321B
- Authority
- CN
- China
- Prior art keywords
- image
- preference
- evaluation value
- images
- operation parameter
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Classifications
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N23/00—Cameras or camera modules comprising electronic image sensors; Control thereof
- H04N23/60—Control of cameras or camera modules
- H04N23/62—Control of parameters via user interfaces
Landscapes
- Engineering & Computer Science (AREA)
- Human Computer Interaction (AREA)
- Multimedia (AREA)
- Signal Processing (AREA)
- User Interface Of Digital Computer (AREA)
- Image Analysis (AREA)
Abstract
The embodiment of the application discloses an image display method and a related product, wherein the method comprises the following steps: acquiring a historical operation parameter set of each image in a plurality of images to obtain a plurality of historical operation parameter sets; evaluating the preference degree of each image in the multiple images according to the multiple historical operating parameter sets to obtain multiple evaluation values, wherein each image corresponds to one evaluation value, and the preference degree is used for expressing the preference degree of a target object to the images; classifying a plurality of images according to a preset deep learning model to obtain a plurality of types of images, wherein each type of image at least comprises one image; determining an evaluation value corresponding to each type of image in the multi-type images to obtain a plurality of evaluation value sets, wherein each type of image corresponds to one evaluation value set; determining a preference grade corresponding to each type of image in the multiple types of images according to the multiple evaluation value sets to obtain multiple preference grades; and displaying the multiple types of images according to the multiple preference grades. The photo album display method and the photo album display device can achieve the photo album display function according to the preference degree.
Description
Technical Field
The application relates to the technical field of electronic equipment, in particular to an image display method and a related product.
Background
With the widespread use of electronic devices (such as mobile phones, tablet computers, and the like), the electronic devices can support more and more applications and have more and more powerful functions, and the electronic devices can not only take pictures, but also realize various ps (photoshop) functions.
At present, the electronic device cannot know the preference degree of the user to the image, and further cannot accurately display the image.
Disclosure of Invention
The embodiment of the application provides an image display method and a related product, which can accurately display images and improve the intelligence and convenience of electronic equipment.
In a first aspect, an embodiment of the present application provides an image display method, including:
obtaining a historical operation parameter set of each image in a plurality of images to obtain a plurality of historical operation parameter sets, wherein each historical operation parameter set comprises at least one operation parameter, and each operation parameter is used for expressing an operation action on the image;
evaluating the preference degree of each image in the multiple images according to the multiple historical operating parameter sets to obtain multiple evaluation values, wherein each image corresponds to one evaluation value, and the preference degree is used for expressing the preference degree of a target object to the image;
classifying the multiple images according to a preset deep learning model to obtain multiple types of images, wherein each type of image at least comprises one image;
determining an evaluation value corresponding to each type of image in the multi-type images to obtain a plurality of evaluation value sets, wherein each type of image corresponds to one evaluation value set;
determining a preference grade corresponding to each type of image in the multiple types of images according to the multiple evaluation value sets to obtain multiple preference grades;
and displaying the multi-class images according to the preference grades.
In a second aspect, an embodiment of the present application provides a picture image display apparatus, the apparatus including:
the device comprises an acquisition unit, a processing unit and a processing unit, wherein the acquisition unit is used for acquiring a historical operation parameter set of each image in a plurality of images to obtain a plurality of historical operation parameter sets, each historical operation parameter set comprises at least one operation parameter, and each operation parameter is used for expressing an operation action on the image;
the evaluation unit is used for evaluating the preference degree of each image in the plurality of images according to the plurality of historical operating parameter sets to obtain a plurality of evaluation values, each image corresponds to one evaluation value, and the preference degree is used for expressing the preference degree of a target object to the images;
the classification unit is used for classifying the multiple images according to a preset deep learning model to obtain multiple types of images, and each type of image at least comprises one image;
the determining unit is used for determining an evaluation value corresponding to each type of image in the multiple types of images to obtain multiple evaluation value sets, and each type of image corresponds to one evaluation value set; determining a preference grade corresponding to each type of image in the multiple types of images according to the multiple evaluation value sets to obtain multiple preference grades;
and the display unit is used for displaying the multi-class images according to the preference grades.
In 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, where the one or more programs are stored in the memory and configured to be executed by the processor, and the program includes instructions for executing the steps in the first aspect of the embodiment of the present application.
In 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, where the computer program enables a computer to perform some or all of the steps described in the first aspect of the embodiment of the present application.
In a fifth aspect, embodiments of the present application provide a computer program product, where the computer program product includes a non-transitory computer-readable storage medium storing a computer program, where the computer program is operable to cause a computer to perform some or all of the steps as described in the first aspect of the embodiments of the present application. The computer program product may be a software installation package.
It can be seen that, the image display method and the related product described in the embodiments of the present application are applied to an electronic device, obtain a plurality of historical operation parameter sets of each image of a plurality of images, each historical operation parameter set includes at least one operation parameter, each operation parameter is used for expressing an operation action on an image, perform preference evaluation on each image of the plurality of images according to the plurality of historical operation parameter sets to obtain a plurality of evaluation values, each image corresponds to an evaluation value, the preference is used for expressing a preference of a target object to the image, classify the plurality of images according to a preset deep learning model to obtain a plurality of classes of images, each class of images includes at least one image, determine an evaluation value corresponding to each class of images of the plurality of classes of images to obtain a plurality of evaluation value sets, each class of images corresponds to one evaluation value set, the preference grade corresponding to each type of image in the multi-type image is determined according to the plurality of evaluation value sets, a plurality of preference grades are obtained, and the multi-type image is displayed according to the preference grades, so that the preference degree of each type of image can be determined, further, the album display function is realized according to the preference degree, and the intelligence and the convenience of the electronic equipment are improved.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1A is a schematic structural diagram of an electronic device according to an embodiment of the present disclosure;
fig. 1B is a schematic flowchart of an image displaying method according to an embodiment of the present disclosure;
FIG. 2 is a schematic flow chart of another image displaying method provided in the embodiments of the present application;
fig. 3 is a schematic structural diagram of an electronic device according to an embodiment of the present application;
FIG. 4A is a block diagram illustrating functional units of an image displaying apparatus according to an embodiment of the present disclosure;
fig. 4B is a block diagram of functional units of another image displaying apparatus according to an embodiment of the present disclosure.
Detailed Description
In order to make the technical solutions of the present application better understood, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, 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 in the above-described drawings are used for distinguishing between different objects and not for describing a particular order. Furthermore, the terms "include" and "have," as well as any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not limited to only those steps or elements listed, but may alternatively include other steps or elements not listed, or inherent to such process, method, article, or apparatus.
Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the application. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. It is explicitly and implicitly understood by one skilled in the art that the embodiments described herein can be combined with other embodiments.
The electronic device related to the embodiments of the present application may include various handheld devices, vehicle-mounted devices, wearable devices (smart watches, smart bracelets, wireless headsets, augmented reality/virtual reality devices, smart glasses), computing devices or other processing devices connected to wireless modems, and various forms of User Equipment (UE), Mobile Stations (MS), terminal devices (terminal device), and the like, which have wireless communication functions. For convenience of description, the above-mentioned devices are collectively referred to as electronic devices.
The following describes embodiments of the present application in detail.
Referring to fig. 1A, fig. 1A is a schematic structural diagram of an electronic device according to an embodiment of the present disclosure, where 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 circuitry may include, among other things, storage and processing circuitry. The storage circuit in the storage and processing circuit may be a memory, such as a hard disk drive memory, a non-volatile memory (e.g., a flash memory or other electronically programmable read only memory used to form a solid state drive, etc.), a volatile memory (e.g., a static or dynamic random access memory, etc.), etc., and the embodiments of the present application are not limited thereto. Processing circuitry in the storage and processing circuitry may be used to control the operation of the electronic device. The processing circuitry may be implemented 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 alternatively, quantum chips, nanochip chips, and the like.
The storage and processing circuitry may be used to run software in the electronic device, such as play incoming call alert ringing application, play short message alert ringing application, play alarm alert ringing application, play media file application, Voice Over Internet Protocol (VOIP) phone call application, operating system functions, and so forth. The software may be used to perform some control operations, such as playing an incoming alert ring, playing a short message alert ring, playing an alarm alert ring, playing a media file, making a voice phone call, and performing other functions in the electronic device, and the embodiments of the present application are not limited.
The input-output circuit can be used for enabling the electronic device to input and output data, namely allowing the electronic device to receive data from the external device and allowing the electronic device to output data from the electronic device to the external device.
The input-output circuit may further include a sensor. The sensors may include ambient light sensors, optical and capacitive based infrared proximity sensors, fingerprint sensors, ultrasonic sensors, touch sensors (e.g., optical and/or capacitive based touch sensors, where the touch sensors may be part of a touch display screen or may be used independently as a touch sensor structure), acceleration sensors, gravity sensors, and other sensors, and the like, the fingerprint sensors being at least one of: a capacitive fingerprint sensor, an ultrasonic fingerprint sensor, an optical fingerprint sensor, etc., without limitation. The input-output circuit may further include audio components that may be used to provide audio input and output functionality for the electronic device. The audio components may also include a tone generator and other components for generating and detecting sound.
The input-output circuit may also include a plurality of display screens. Above-mentioned a plurality of display screens include 2 or more than 2 display screens, a plurality of display screens can set up respectively with electronic equipment's front and back, certainly in practical application, a plurality of display screens can also set up in other positions, the display screen can include liquid crystal display, organic light emitting diode display screen, electron ink display screen, plasma display screen, use in the display screen of other display technologies one or several kinds of combinations. The display screen may include an array of touch sensors (i.e., the display screen may be a touch display screen). The touch sensor may be a capacitive touch sensor formed by a transparent touch sensor electrode (e.g., an Indium Tin Oxide (ITO) electrode) array, or may be a touch sensor formed using other touch technologies, such as acoustic wave touch, pressure sensitive touch, resistive touch, optical touch, and the like, and the embodiments of the present application are not limited thereto.
The input-output circuitry may further include communications circuitry that may be used to provide the electronic device with the ability to communicate with external devices. The communication circuitry may include analog and digital input-output interface circuitry, and wireless communication circuitry based on radio frequency signals and/or optical signals. The wireless communication circuitry in the communication circuitry may include radio frequency transceiver circuitry, power amplifier circuitry, low noise amplifiers, switches, filters, and antennas. For example, the wireless communication circuitry in the communication circuitry may include circuitry to support Near Field Communication (NFC) by transmitting and receiving near field coupled electromagnetic signals. For example, the communication circuit may include a near field communication antenna and a near field communication transceiver. The communications circuitry may also include cellular telephone transceiver and antennas, wireless local area network transceiver circuitry and antennas, and so forth.
The input-output circuit may further include other input-output units. Input-output units may include buttons, joysticks, click wheels, scroll wheels, touch pads, keypads, keyboards, cameras, light emitting diodes and other status indicators, and the like.
The electronic device may further include a battery (not shown) for supplying power to the electronic device.
Referring to fig. 1B, fig. 1B is a schematic flowchart of an image displaying method according to an embodiment of the present disclosure, as shown in the figure, applied to the electronic device shown in fig. 1A, the image displaying method includes:
101. the method comprises the steps of obtaining a historical operation parameter set of each image in a plurality of images to obtain a plurality of historical operation parameter sets, wherein each historical operation parameter set comprises at least one operation parameter, and each operation parameter is used for expressing an operation action on the image.
Wherein, a plurality of images can be stored in the electronic equipment in advance. The user may perform different operation actions on each image, for example, opening the image, closing the image, deleting the image, restoring the image, forwarding the image, beautifying the image, collecting the image, and the like, which are not limited herein, so that different operation parameters may be generated, for example, the time length of opening the image may be recorded when the image is opened, for example, the time length of recording the image when the image is collected may be recorded when the image is collected, and for example, the time length of recording the operation parameter when the image is not collected may be 1 when the image is collected, and the time length of recording the operation parameter when the image is not collected may be 0. Each image may correspond to one historical operation parameter set, the plurality of images may correspond to a plurality of historical operation parameter sets, each historical operation parameter set may correspond to one operation parameter, and each operation parameter is used to describe an operation action performed on the image. Of course, the historical set of operation parameters for each image may be maintained in an image operation history database.
Optionally, each of the historical operation parameter sets may include at least one operation parameter, and the operation parameter may be at least one of: the average residence time of the image (i.e. the average time of the user displaying the image), whether to collect the image, whether to forward the image, the number of times to delete the image, whether to restore the image, etc., whether to puzzle the image, etc., which are not limited herein.
Optionally, in step 101, obtaining a plurality of historical operation parameter sets of each of the plurality of images, and obtaining the plurality of historical operation parameter sets, may be implemented as follows:
and acquiring a historical operation parameter set of each image in the plurality of images in a preset time period to obtain a plurality of historical operation parameter sets.
Wherein, the preset time period can be set by the user or the default of the system. For example, in real life, a user may be interested in a certain image for a period of time, and may not be interested in the image for another period of time, so that the operation of the time period in which the user is interested may be reasonably selected.
102. And evaluating the preference degree of each image in the plurality of images according to the plurality of historical operating parameter sets to obtain a plurality of evaluation values, wherein each image corresponds to one evaluation value, and the preference degree is used for expressing the preference degree of the target object to the image.
Each historical operation set in the multiple historical operation parameter sets comprises at least one operation parameter, and then preference evaluation can be carried out on the images according to the operation parameters. Therefore, preference evaluation may be performed on each of the plurality of images based on the plurality of sets of historical operating parameters, to obtain a plurality of evaluation values, one for each image, the preference representing a degree of preference of a target object for the image, where the target object may be a user or a host of the electronic device.
In the step 102, evaluating a preference degree of each of the plurality of images according to the plurality of historical operating parameter sets to obtain a plurality of evaluation values, the method may include:
21. obtaining a weight corresponding to each operation parameter in the historical operation parameter set i to obtain at least one weight, wherein the historical operation parameter set i is any one of the plurality of historical operation parameter sets;
22. and performing weighting operation according to the operation parameters in the historical operation parameter set i and the at least one weight value to obtain an evaluation value.
Taking a historical operation parameter set i as an example, the historical operation parameter set i is any one of a plurality of historical operation parameter sets, each historical operation parameter set includes at least one operation parameter, each operation parameter corresponds to at least one weight, a mapping relationship between the operation parameter and the weight may be stored in the electronic device in advance, and then the weight corresponding to each operation parameter may be determined according to the mapping relationship. Assuming that the historical operation parameter set i includes n operation parameters, each operation parameter corresponds to a weight, and each weight can be between 0 and 1, as follows:
| operating parameters | Weight value |
| Operating parameters 1 | Weight 1 |
| Operating parameters 2 | Weight 2 |
| … | … |
| Operating parameter n | Weight n |
Furthermore, a weighting operation may be performed according to the operation parameters in the historical operation parameter i and at least one weight, and the evaluation value is operation parameter 1 × weight 1+ operation parameter 2 × weight 2+ … + operation parameter n × weight n, so that the evaluation value corresponding to each historical operation parameter set can be realized.
103. And classifying the plurality of images according to a preset deep learning model to obtain a plurality of types of images, wherein each type of image at least comprises one image.
The preset deep learning model may be at least one of the following: the convolutional neural network deep learning model, the Adaboost deep learning model and the random forest deep learning model are not repeated here, and in specific implementation, the electronic device can train a positive sample image and a negative sample image for collecting each type of image, and then obtain a preset deep learning model. Specifically, the electronic device may classify a plurality of images according to a preset deep learning model to obtain a plurality of types of images, where each type of image includes at least one image, and each type of image may also correspond to one type identifier. The category identification may be at least one of: night portrait, beach, snow, baby, pet, without limitation.
Optionally, the steps 102 and 103 may be executed in parallel, or the step 103 may be executed before the step 102, which is not limited herein.
104. And determining an evaluation value corresponding to each type of image in the multi-type images to obtain a plurality of evaluation value sets, wherein each type of image corresponds to one evaluation value set.
Each type of image comprises at least one image, each image corresponds to one evaluation value, and further, each type of image can correspond to one evaluation value set, and multiple types of images correspond to multiple evaluation value sets.
105. And determining the preference grade corresponding to each type of image in the multi-type images according to the evaluation value sets to obtain a plurality of preference grades.
The evaluation value reflects the preference degree of the user for a single image to a certain degree, and the evaluation value set reflects the preference degree of the user for a class of images to a certain degree, so that the preference grade corresponding to each class of images in the multiple classes of images can be determined according to the multiple evaluation value sets, and multiple preference grades are obtained, for example, the preference grade corresponding to each class of images can be determined according to the average evaluation value in the multiple evaluation value sets, that is, the mapping relationship between the average evaluation value and the preference grade is stored in advance, and further, the preference grade corresponding to each average evaluation value can be determined, for example: the preference level is higher if the average evaluation value is larger, and for example, the preference level corresponding to each type of image may be determined according to the sum of evaluation values in a plurality of evaluation value sets, that is, the mapping relationship between the evaluation values and the preference levels may be stored in advance, and further, the preference level corresponding to each evaluation value sum may be determined, for example: the larger the total sum of evaluation values is, the higher the preference rank is.
Optionally, in step 105, determining a preference level corresponding to each image in the multiple types of images according to the multiple evaluation value sets to obtain multiple preference levels, and the method may include the following steps:
51. respectively determining the number of evaluation values and the mean value of the evaluation values of each evaluation value set in the plurality of evaluation value sets to obtain a plurality of evaluation value numbers and a plurality of mean values of the evaluation values;
52. acquiring a first weight corresponding to the number of evaluation values in each of a plurality of evaluation value sets and a second weight corresponding to the mean value of the evaluation values;
53. performing weighting operation according to the evaluation value number, the evaluation value mean value, the first weight and the second weight corresponding to each evaluation value set in the plurality of evaluation value sets to obtain a plurality of preference values;
54. and determining the preference grade corresponding to each preference value in the preference values according to a preset mapping relation between the preference values and the preference grades to obtain a plurality of preference grades.
In the present application, considering some types of images, the number of the images may be large, but the average evaluation value is low, or the total evaluation value is low, so that the preference of the user for a certain type of images is comprehensively evaluated in combination with two dimensions of the number of the evaluation values and the mean evaluation value, in a specific implementation, the electronic device may determine the number of the evaluation values and the mean evaluation value corresponding to each evaluation value set in a plurality of evaluation value sets to obtain a plurality of numbers of the evaluation values and a plurality of mean evaluation values, a mapping relationship between the number of the evaluation values and a first weight may be stored in the electronic device in advance, further, a first weight corresponding to the number of the evaluation values in each evaluation value set in the plurality of evaluation value sets may be determined according to the mapping relationship, and a second weight corresponding to each evaluation value set in the plurality of evaluation value sets may be determined according to the mapping relationship, further, a plurality of preference values are obtained by performing a weighted operation according to the number of evaluation values, the mean evaluation value, the first weight, and the second weight corresponding to each of the plurality of evaluation value sets, specifically, for any evaluation value set, the corresponding preference value is the number of evaluation values + the first weight + the average evaluation value + the second weight, and a mapping relationship between a preset preference value and a preference rank may be stored in the electronic device in advance. For example, the preference level may be: preferably, preferred, and common, but not limited thereto.
106. And displaying the multi-class images according to the preference grades.
The preference levels of different users indicate the preference levels of the images to a certain extent, so that in specific implementation, the higher the preference level is, the priority is to display the images corresponding to the preference level.
Optionally, in step 106, displaying the multiple types of images according to the multiple preference levels may include the following steps:
61. selecting at least one target image with the largest evaluation value from each image in the multiple types of images;
62. taking the at least one target image as a cover image to obtain a plurality of cover images, wherein each type of image corresponds to one cover image;
63. determining the display order of the multiple types of images according to the multiple preference grades;
64. and displaying the cover images according to the display sequence.
The multiple preference levels may form a priority order, and then, the display order of the multiple types of images is determined according to the multiple preference levels, for example, the higher the preference level is, the higher the priority is, the more the display is prioritized. Each image in multiple types of images in the electronic device corresponds to one evaluation value, so that a target image corresponding to the maximum evaluation value in each image type can be obtained, of course, each image type may also include multiple target images corresponding to multiple maximum evaluation values, further, the at least one target image may be used as a cover image, each image type corresponds to one cover image, the multiple target images may be used as one cover image in a mosaic form, finally, the multiple cover images are displayed according to the display sequence, each cover image corresponds to one image type, and when a user clicks the cover image, all the images in the image type can be displayed.
Optionally, before the step 101, the following steps may be further included:
a1, acquiring an image library;
a2, acquiring a preset identification set;
and A3, screening 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 method includes the steps of presetting a time period, presetting a storage path, presetting a format type, presetting a photographing mode, presetting a camera, presetting a photographing place and the like, and is not limited herein. Specifically, the electronic device may acquire an image library, and of course, the image library may include a large number of images, and then, the images may be screened, that is, a preset identification set may be acquired, and the image library may be screened according to the preset identification set, so as to obtain a plurality of images.
For example, the electronic device may set a preset identification set, such as: an image file type white list (specified image format), such as a jpg format, a bmp format, a png format, or a white list (specified path) of a path where an image is located is reserved, such as only a storage/0/DCIM directory is reserved, further, an image operation history database is established, an open function and a close function in a system libc are modified, whether the opened image is the image in the specified image format in the specified path is judged each time when the file is opened, if so, an opened file name, path information and opening time are recorded in the image operation history database, when the image is closed, an image closing time point is recorded in the image operation history database, and the image in the database is classified by adopting a preset deep learning model, for example, the category identification can be divided into: the method comprises the following steps of (1) counting the opening times of various images and the average residence time of various images according to operation data (such as the number of times of opening the images and the average residence time of the images) and classification results in an image operation historical record database at night, on beaches, snowfields, infants, pets and the like, certainly, calculating the time interval between the opening time and the creation time of each image according to the data in the database, normalizing the residence time and the time interval to a numerical value interval of 1-5, classifying each image in an album to obtain a category, calculating the time interval between the image and the creation time, and calculating the category score of the image, wherein the category score is specifically as follows: the opening times and the average residence time of each type can be added, the category score of each image and the time interval of the distance creation time are subjected to weighted summation, the obtained numerical value is used as a potential preference numerical value of the image of the user, the preference grade corresponding to each type of image is determined according to the preference numerical value of the image in the album, and the multiple types of images are displayed according to the preference grade.
It can be seen that the image display method described in this embodiment of the present application is applied to an electronic device, and is implemented by obtaining a plurality of historical operation parameter sets of each of a plurality of images, obtaining a plurality of historical operation parameter sets, each historical operation parameter set including at least one operation parameter, each operation parameter being used for expressing an operation action on an image, performing preference evaluation on each of the plurality of images according to the plurality of historical operation parameter sets to obtain a plurality of evaluation values, each image corresponding to one evaluation value, the preference being used for expressing a preference of a target object to the image, classifying the plurality of images according to a preset deep learning model to obtain a plurality of classes of images, each class of images including at least one image, determining an evaluation value corresponding to each class of image in the plurality of classes of images to obtain a plurality of evaluation value sets, each class of image corresponding to one evaluation value set, determining a preference level corresponding to each class of image in the plurality of classes of images according to the plurality of evaluation value sets, obtain a plurality of preference levels, demonstrate multiclass image according to a plurality of preference levels to, can confirm the preference degree of every type of image, and then, realize album show function according to the preference degree, promoted electronic equipment's intelligence and convenience.
Referring to fig. 2, fig. 2 is a schematic flow chart of an image displaying method according to an embodiment of the present disclosure, as shown in the figure, applied to the electronic device shown in fig. 1A, where the image displaying method includes:
201. and acquiring an image library.
202. And acquiring a preset identification set.
203. And screening the image library according to the preset identification set to obtain a plurality of images.
204. And acquiring a historical operation parameter set of each image in the plurality of images to obtain a plurality of historical operation parameter sets, wherein each historical operation parameter set comprises at least one operation parameter, and each operation parameter is used for expressing an operation action on the image.
205. And evaluating the preference degree of each image in the plurality of images according to the plurality of historical operating parameter sets to obtain a plurality of evaluation values, wherein each image corresponds to one evaluation value, and the preference degree is used for expressing the preference degree of the target object to the image.
206. And classifying the plurality of images according to a preset deep learning model to obtain a plurality of types of images, wherein each type of image at least comprises one image.
207. And determining an evaluation value corresponding to each type of image in the multi-type images to obtain a plurality of evaluation value sets, wherein each type of image corresponds to one evaluation value set.
208. And determining the preference grade corresponding to each type of image in the multi-type images according to the evaluation value sets to obtain a plurality of preference grades.
209. And displaying the multi-class images according to the preference grades.
The detailed description of the steps 201 to 209 may refer to the corresponding steps of the image displaying method described in the above fig. 1B, and are not repeated herein.
It can be seen that the image display method described in the embodiment of the present application is applied to an electronic device, and includes obtaining an image library, obtaining a preset identifier set, screening the image library according to the preset identifier set, obtaining a plurality of images, obtaining a plurality of historical operation parameter sets of each of the plurality of images, obtaining a plurality of historical operation parameter sets, each of the historical operation parameter sets including at least one operation parameter, each of the operation parameters being used for expressing an operation action on an image, performing preference evaluation on each of the plurality of images according to the plurality of historical operation parameter sets, obtaining a plurality of evaluation values, each of the images corresponding to one evaluation value, the preference being used for expressing a preference degree of a target object to the image, classifying the plurality of images according to a preset deep learning model, obtaining a plurality of types of images, each of the types of images including at least one image, determining the evaluation value corresponding to each of the plurality of types of images, the method comprises the steps of obtaining a plurality of evaluation value sets, enabling each type of image to correspond to one evaluation value set, determining preference levels corresponding to each type of image in the plurality of types of images according to the plurality of evaluation value sets, obtaining a plurality of preference levels, displaying the plurality of types of images according to the plurality of preference levels, determining preference degrees of each type of image, further realizing album display functions according to the preference degrees, and improving intelligence and convenience of electronic equipment.
Referring to fig. 3, fig. 3 is a schematic structural diagram of an electronic device according to an embodiment of the present disclosure, as shown in the figure, the electronic device includes a processor, a memory, a communication interface, and one or more programs, where the one or more programs are stored in the memory and configured to be executed by the processor, and in an embodiment of the present disclosure, the electronic device is in a screen-off state, and the programs include instructions for performing the following steps:
obtaining a historical operation parameter set of each image in a plurality of images to obtain a plurality of historical operation parameter sets, wherein each historical operation parameter set comprises at least one operation parameter, and each operation parameter is used for expressing an operation action on the image;
evaluating the preference degree of each image in the multiple images according to the multiple historical operating parameter sets to obtain multiple evaluation values, wherein each image corresponds to one evaluation value, and the preference degree is used for expressing the preference degree of a target object to the image;
classifying the multiple images according to a preset deep learning model to obtain multiple types of images, wherein each type of image at least comprises one image;
determining an evaluation value corresponding to each type of image in the multi-type images to obtain a plurality of evaluation value sets, wherein each type of image corresponds to one evaluation value set;
determining a preference grade corresponding to each type of image in the multiple types of images according to the multiple evaluation value sets to obtain multiple preference grades;
and displaying the multi-class images according to the preference grades.
It can be seen that, in the electronic device described in this embodiment of the present application, a historical operation parameter set of each of a plurality of images is obtained, a plurality of historical operation parameter sets are obtained, each historical operation parameter set includes at least one operation parameter, each operation parameter is used for expressing an operation action on the image, preference evaluation is performed on each of the plurality of images according to the plurality of historical operation parameter sets, a plurality of evaluation values are obtained, each image corresponds to one evaluation value, the preference is used for expressing a preference degree of a target object for the image, the plurality of images are classified according to a preset deep learning model, a plurality of classes of images are obtained, each class of image includes at least one image, an evaluation value corresponding to each class of image in the plurality of classes of images is determined, a plurality of evaluation value sets are obtained, each class of image corresponds to one evaluation value set, a preference level corresponding to each class of image in the plurality of classes of images is determined according to the plurality of evaluation value sets, obtain a plurality of preference levels, demonstrate multiclass image according to a plurality of preference levels to, can confirm the preference degree of every type of image, and then, realize album show function according to the preference degree, promoted electronic equipment's intelligence and convenience.
In one possible example, in the evaluating a preference degree of each of the plurality of images according to the plurality of sets of historical operating parameters, obtaining a plurality of evaluation values, the program includes instructions for:
obtaining a weight corresponding to each operation parameter in the historical operation parameter set i to obtain at least one weight, wherein the historical operation parameter set i is any one of the historical operation parameter sets;
and performing weighting operation according to the operation parameters in the historical operation parameter set i and the at least one weight value to obtain an evaluation value.
In one possible example, the program further includes instructions for performing the steps of:
acquiring an image library;
acquiring a preset identification set;
and screening the image library according to the preset identification set to obtain the plurality of images.
In one possible example, in the determining the preference level corresponding to each image of the multiple types of images according to the multiple evaluation value sets to obtain multiple preference levels, the program includes instructions for:
respectively determining the number of evaluation values and the mean value of the evaluation values of each evaluation value set in the plurality of evaluation value sets to obtain a plurality of evaluation value numbers and a plurality of mean values of the evaluation values;
acquiring a first weight corresponding to the number of evaluation values in each of a plurality of evaluation value sets and a second weight corresponding to the mean value of the evaluation values;
performing weighting operation according to the evaluation value number, the evaluation value mean value, the first weight and the second weight corresponding to each evaluation value set in the plurality of evaluation value sets to obtain a plurality of preference values;
and determining the preference grade corresponding to each preference value in the preference values according to a preset mapping relation between the preference values and the preference grades to obtain a plurality of preference grades.
In one possible example, in said presenting said plurality of categories of images in accordance with said plurality of preference ratings, the above program includes instructions for:
selecting at least one target image with the largest evaluation value from each image in the multiple types of images;
taking the at least one target image as a cover image to obtain a plurality of cover images, wherein each type of image corresponds to one cover image;
determining the display order of the multiple types of images according to the multiple preference grades;
and displaying the cover images according to the display sequence.
The above description has introduced the solution of the embodiment of the present application mainly from the perspective of the method-side implementation process. It is understood that the electronic device comprises corresponding hardware structures and/or software modules for performing the respective functions in order to realize the above-mentioned functions. Those of skill in the art will readily appreciate that the present application is capable of hardware or a combination of hardware and computer software implementing the various illustrative elements and algorithm steps described in connection with the embodiments provided herein. Whether a function is performed as hardware or computer software drives hardware depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
In the embodiment of the present application, the electronic device may be divided into the functional units according to the method example, 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 integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit. It should be noted that the division of the unit in the embodiment of the present application is schematic, and is only a logic function division, and there may be another division manner in actual implementation.
Fig. 4A is a block diagram of functional unit components of the image presentation apparatus 400 referred to in the embodiment of the present application. The image presentation apparatus 400 is applied to an electronic device, and the apparatus 400 includes an obtaining unit 401, an evaluating unit 402, a classifying unit 403, a determining unit 404, and a presentation unit 405, wherein,
an obtaining unit 401, configured to obtain a plurality of historical operation parameter sets of each of a plurality of images, to obtain a plurality of historical operation parameter sets, where each historical operation parameter set includes at least one operation parameter, and each operation parameter is used to express an operation action on an image;
an evaluation unit 402, configured to perform preference evaluation on each of the plurality of images according to the plurality of sets of historical operating parameters to obtain a plurality of evaluation values, where each image corresponds to one evaluation value, and the preference is used to indicate a preference degree of a target object for the image;
a classifying unit 403, configured to classify the multiple images according to a preset deep learning model to obtain multiple classes of images, where each class of image includes at least one image;
a determining unit 404, configured to determine an evaluation value corresponding to each type of image in the multiple types of images, to obtain multiple evaluation value sets, where each type of image corresponds to one evaluation value set; determining a preference grade corresponding to each type of image in the multiple types of images according to the multiple evaluation value sets to obtain multiple preference grades;
a display unit 405, configured to display the multiple types of images according to the multiple preference levels.
It can be seen that, the image display apparatus described in the embodiment of the present application is applied to an electronic device, obtains a plurality of historical operation parameter sets of each image in a plurality of images, obtains a plurality of historical operation parameter sets, each historical operation parameter set includes at least one operation parameter, each operation parameter is used for expressing an operation action on the image, performs preference evaluation on each image in the plurality of images according to the plurality of historical operation parameter sets, obtains a plurality of evaluation values, each image corresponds to one evaluation value, the preference is used for expressing a preference degree of a target object to the image, classifies the plurality of images according to a preset deep learning model, obtains a plurality of types of images, each type of image includes at least one image, determines an evaluation value corresponding to each type of image in the plurality of types of images, obtains a plurality of evaluation value sets, each type of image corresponds to one evaluation value set, determines a preference level corresponding to each type of image in the plurality of types of images according to the plurality of evaluation value sets, obtain a plurality of preference levels, demonstrate multiclass image according to a plurality of preference levels to, can confirm the preference degree of every type of image, and then, realize album show function according to the preference degree, promoted electronic equipment's intelligence and convenience.
In a possible example, in terms of the evaluating the preference degree of each of the plurality of images according to the plurality of sets of historical operating parameters to obtain a plurality of evaluation values, the evaluation unit 402 is specifically configured to:
obtaining a weight corresponding to each operation parameter in the historical operation parameter set i to obtain at least one weight, wherein the historical operation parameter set i is any one of the historical operation parameter sets;
and performing weighting operation according to the operation parameters in the historical operation parameter set i and the at least one weight value to obtain an evaluation value.
In one possible example, as shown in fig. 4B, fig. 4B is a further modified structure of the image display apparatus shown in fig. 4A, which may further include, compared with fig. 4A: the screening unit 406 specifically includes the following:
the obtaining unit 401 is further specifically configured to obtain an image library; acquiring a preset identification set;
the screening unit 406 is configured to screen the image library according to the preset identifier set, so as to obtain the multiple images.
In a possible example, in the aspect that the preference level corresponding to each type of image in the multiple types of images is determined according to the multiple evaluation value sets, so as to obtain multiple preference levels, the determining unit 404 is specifically configured to:
respectively determining the number of evaluation values and the mean value of the evaluation values of each evaluation value set in the plurality of evaluation value sets to obtain a plurality of evaluation value numbers and a plurality of mean values of the evaluation values;
acquiring a first weight corresponding to the number of evaluation values in each of a plurality of evaluation value sets and a second weight corresponding to the mean value of the evaluation values;
performing weighting operation according to the evaluation value number, the evaluation value mean value, the first weight and the second weight corresponding to each evaluation value set in the plurality of evaluation value sets to obtain a plurality of preference values;
and determining the preference grade corresponding to each preference value in the preference values according to a preset mapping relation between the preference values and the preference grades to obtain a plurality of preference grades.
In one possible example, in the aspect of presenting the multiple classes of images according to the multiple preference ranks, the presenting unit 405 is specifically configured to:
selecting at least one target image with the largest evaluation value from each image in the multiple types of images;
taking the at least one target image as a cover image to obtain a plurality of cover images, wherein each type of image corresponds to one cover image;
determining the display order of the multiple types of images according to the multiple preference grades;
and displaying the cover images according to the display sequence.
Embodiments of the present application also provide a computer storage medium, where the computer storage medium stores a computer program for electronic data exchange, the computer program enabling a computer to execute part or all of the steps of any one of the methods described in the above method embodiments, and the computer includes an electronic device.
Embodiments of the present application also provide a computer program product comprising a non-transitory computer readable storage medium storing a computer program operable to cause a computer to perform some or all of the steps of any of the methods as described in the above method embodiments. The computer program product may be a software installation package, the computer comprising an electronic device.
It should be noted that, for simplicity of description, the above-mentioned method embodiments are described as a series of acts or combination of acts, but those skilled in the art will recognize that the present application is not limited by the order of acts described, as some steps may occur in other orders or concurrently depending on the application. Further, those skilled in the art should also appreciate that the embodiments described in the specification are preferred embodiments and that the acts and modules referred to are not necessarily required in this application.
In the foregoing embodiments, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus may be implemented in other manners. For example, the above-described embodiments of the apparatus are merely illustrative, and for example, the above-described division of the units is only one type of division of logical functions, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection of some interfaces, devices or units, and may be an electric or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit may be stored in a computer readable memory if it is implemented in the form of a software functional unit and sold or used as a stand-alone product. Based on such understanding, the technical solution of the present application may be substantially implemented or a part of or all or part of the technical solution contributing to the prior art may be embodied in the form of a software product stored in a memory, and including several instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the above-mentioned method of the embodiments of the present application. And the aforementioned memory comprises: a U-disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a removable hard disk, a magnetic or optical disk, and other various media capable of storing program codes.
Those skilled in the art will appreciate that all or part of the steps in the methods of the above embodiments may be implemented by associated hardware instructed by a program, which may be stored in a computer-readable memory, which may include: flash Memory disks, Read-Only memories (ROMs), Random Access Memories (RAMs), magnetic or optical disks, and the like.
The foregoing detailed description of the embodiments of the present application has been presented to illustrate the principles and implementations of the present application, and the above description of the embodiments is only provided to help understand the method and the core concept of the present application; meanwhile, for a person skilled in the art, according to the idea of the present application, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present application.
Claims (9)
1. An image presentation method, comprising:
obtaining a historical operation parameter set of each image in a plurality of images to obtain a plurality of historical operation parameter sets, wherein each historical operation parameter set comprises at least one operation parameter, and each operation parameter is used for expressing an operation action on the image;
evaluating the preference degree of each image in the multiple images according to the multiple historical operating parameter sets to obtain multiple evaluation values, wherein each image corresponds to one evaluation value, and the preference degree is used for expressing the preference degree of a target object to the image;
classifying the multiple images according to a preset deep learning model to obtain multiple types of images, wherein each type of image at least comprises one image;
determining an evaluation value corresponding to each type of image in the multi-type images to obtain a plurality of evaluation value sets, wherein each type of image corresponds to one evaluation value set;
determining a preference grade corresponding to each type of image in the multiple types of images according to the multiple evaluation value sets to obtain multiple preference grades;
displaying the multi-class images according to the preference grades;
determining a preference grade corresponding to each image in the multiple types of images according to the multiple evaluation value sets to obtain multiple preference grades, including:
respectively determining the number of evaluation values and the mean value of the evaluation values of each evaluation value set in the plurality of evaluation value sets to obtain a plurality of evaluation value numbers and a plurality of mean values of the evaluation values;
acquiring a first weight corresponding to the number of evaluation values in each of a plurality of evaluation value sets and a second weight corresponding to the mean value of the evaluation values;
performing weighting operation according to the evaluation value number, the evaluation value mean value, the first weight and the second weight corresponding to each evaluation value set in the plurality of evaluation value sets to obtain a plurality of preference values;
and determining the preference grade corresponding to each preference value in the preference values according to a preset mapping relation between the preference values and the preference grades to obtain a plurality of preference grades.
2. The method of claim 1, wherein the evaluating a preference of each of the plurality of images according to the plurality of sets of historical operating parameters, resulting in a plurality of evaluation values, comprises:
obtaining a weight corresponding to each operation parameter in the historical operation parameter set i to obtain at least one weight, wherein the historical operation parameter set i is any one of the historical operation parameter sets;
and performing weighting operation according to the operation parameters in the historical operation parameter set i and the at least one weight value to obtain an evaluation value.
3. The method according to claim 1 or 2, characterized in that the method further comprises:
acquiring an image library;
acquiring a preset identification set;
and screening the image library according to the preset identification set to obtain the plurality of images.
4. The method according to claim 1 or 2, wherein said presenting the multi-class image according to the plurality of preference ranks comprises:
selecting at least one target image with the largest evaluation value from each image in the multiple types of images;
taking the at least one target image as a cover image to obtain a plurality of cover images, wherein each type of image corresponds to one cover image;
determining the display order of the multiple types of images according to the multiple preference grades;
and displaying the cover images according to the display sequence.
5. An image presentation device, the device comprising:
the device comprises an acquisition unit, a processing unit and a processing unit, wherein the acquisition unit is used for acquiring a historical operation parameter set of each image in a plurality of images to obtain a plurality of historical operation parameter sets, each historical operation parameter set comprises at least one operation parameter, and each operation parameter is used for expressing an operation action on the image;
the evaluation unit is used for evaluating the preference degree of each image in the plurality of images according to the plurality of historical operating parameter sets to obtain a plurality of evaluation values, each image corresponds to one evaluation value, and the preference degree is used for expressing the preference degree of a target object to the images;
the classification unit is used for classifying the multiple images according to a preset deep learning model to obtain multiple types of images, and each type of image at least comprises one image;
the determining unit is used for determining an evaluation value corresponding to each type of image in the multiple types of images to obtain multiple evaluation value sets, and each type of image corresponds to one evaluation value set; determining a preference grade corresponding to each type of image in the multiple types of images according to the multiple evaluation value sets to obtain multiple preference grades;
the display unit is used for displaying the multi-class images according to the preference grades;
wherein, in the aspect of determining the preference level corresponding to each type of image in the multiple types of images according to the multiple evaluation value sets to obtain multiple preference levels, the determining unit is specifically configured to:
respectively determining the number of evaluation values and the mean value of the evaluation values of each evaluation value set in the plurality of evaluation value sets to obtain a plurality of evaluation value numbers and a plurality of mean values of the evaluation values;
acquiring a first weight corresponding to the number of evaluation values in each of a plurality of evaluation value sets and a second weight corresponding to the mean value of the evaluation values;
performing weighting operation according to the evaluation value number, the evaluation value mean value, the first weight and the second weight corresponding to each evaluation value set in the plurality of evaluation value sets to obtain a plurality of preference values;
and determining the preference grade corresponding to each preference value in the preference values according to a preset mapping relation between the preference values and the preference grades to obtain a plurality of preference grades.
6. The apparatus according to claim 5, wherein in said evaluating a preference degree of each of the plurality of images according to the plurality of sets of historical operating parameters, resulting in a plurality of evaluation values, the evaluation unit is specifically configured to:
obtaining a weight corresponding to each operation parameter in the historical operation parameter set i to obtain at least one weight, wherein the historical operation parameter set i is any one of the historical operation parameter sets;
and performing weighting operation according to the operation parameters in the historical operation parameter set i and the at least one weight value to obtain an evaluation value.
7. The apparatus of claim 5 or 6, further comprising: a screening unit;
the acquiring unit is further specifically used for acquiring an image library; acquiring a preset identification set;
and the screening unit is used for screening the image library according to the preset identification set to obtain the plurality of images.
8. An electronic device comprising a processor, a memory for storing one or more programs and configured for execution by the processor, the programs comprising instructions for performing the steps in the method of any of claims 1-4.
9. A computer-readable storage medium, characterized in that a computer program for electronic data exchange is stored, wherein the computer program causes a computer to perform the method according to any one of claims 1-4.
Priority Applications (2)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811161360.4A CN109508321B (en) | 2018-09-30 | 2018-09-30 | Image display method and related product |
| PCT/CN2019/094901 WO2020063014A1 (en) | 2018-09-30 | 2019-07-05 | Image display method and related product |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811161360.4A CN109508321B (en) | 2018-09-30 | 2018-09-30 | Image display method and related product |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN109508321A CN109508321A (en) | 2019-03-22 |
| CN109508321B true CN109508321B (en) | 2022-01-28 |
Family
ID=65746345
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201811161360.4A Active CN109508321B (en) | 2018-09-30 | 2018-09-30 | Image display method and related product |
Country Status (2)
| Country | Link |
|---|---|
| CN (1) | CN109508321B (en) |
| WO (1) | WO2020063014A1 (en) |
Families Citing this family (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109508321B (en) * | 2018-09-30 | 2022-01-28 | Oppo广东移动通信有限公司 | Image display method and related product |
| CN110008364B (en) * | 2019-03-25 | 2023-05-02 | 联想(北京)有限公司 | Image processing method, device and system |
| CN110595602B (en) * | 2019-04-26 | 2021-10-15 | 深圳市豪视智能科技有限公司 | Vibration detection method and related product |
| WO2019156543A2 (en) * | 2019-04-30 | 2019-08-15 | 엘지전자 주식회사 | Method for determining representative image of video, and electronic device for processing method |
| CN110991373A (en) * | 2019-12-09 | 2020-04-10 | 北京字节跳动网络技术有限公司 | Image processing method, image processing apparatus, electronic device, and medium |
| CN110995999A (en) * | 2019-12-12 | 2020-04-10 | 北京小米智能科技有限公司 | Dynamic photo shooting method and device |
| CN111199540A (en) * | 2019-12-27 | 2020-05-26 | Oppo广东移动通信有限公司 | Image quality evaluation method, image quality evaluation device, electronic device, and storage medium |
| CN113361956B (en) * | 2021-06-29 | 2023-09-15 | 北京百度网讯科技有限公司 | Resource quality evaluation method, device, equipment and storage medium for resource producer |
| CN116313116B (en) * | 2023-05-12 | 2023-07-28 | 氧乐互动(天津)科技有限公司 | Simulation processing system and method based on human body thermal physiological model |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103514254A (en) * | 2013-07-04 | 2014-01-15 | 李文博 | Image set ordering method for mining hidden operation behavior |
| CN106528879A (en) * | 2016-12-14 | 2017-03-22 | 北京小米移动软件有限公司 | Picture processing method and device |
| CN107169150A (en) * | 2017-06-30 | 2017-09-15 | 努比亚技术有限公司 | Picture method for pushing, mobile terminal and computer-readable medium |
| CN107977437A (en) * | 2017-12-06 | 2018-05-01 | 广东欧珀移动通信有限公司 | Image processing method, device, storage medium and electronic equipment |
| CN108038161A (en) * | 2017-12-06 | 2018-05-15 | 北京奇虎科技有限公司 | Information recommendation method, device and computing device based on photograph album |
Family Cites Families (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US8934661B2 (en) * | 2011-12-09 | 2015-01-13 | Facebook, Inc. | Automatic photo album creation based on social information |
| CN102799635B (en) * | 2012-06-27 | 2015-10-28 | 天津大学 | The image collection sort method that a kind of user drives |
| US9298744B2 (en) * | 2014-03-31 | 2016-03-29 | Adobe Systems Incorporated | Method and apparatus for ordering images in an image set based on social interactions and viewer preferences |
| US9412043B2 (en) * | 2014-10-03 | 2016-08-09 | EyeEm Mobile GmbH | Systems, methods, and computer program products for searching and sorting images by aesthetic quality |
| JP6341184B2 (en) * | 2015-11-18 | 2018-06-13 | カシオ計算機株式会社 | Image processing apparatus, image processing method, and program |
| CN109508321B (en) * | 2018-09-30 | 2022-01-28 | Oppo广东移动通信有限公司 | Image display method and related product |
-
2018
- 2018-09-30 CN CN201811161360.4A patent/CN109508321B/en active Active
-
2019
- 2019-07-05 WO PCT/CN2019/094901 patent/WO2020063014A1/en active Application Filing
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103514254A (en) * | 2013-07-04 | 2014-01-15 | 李文博 | Image set ordering method for mining hidden operation behavior |
| CN106528879A (en) * | 2016-12-14 | 2017-03-22 | 北京小米移动软件有限公司 | Picture processing method and device |
| CN107169150A (en) * | 2017-06-30 | 2017-09-15 | 努比亚技术有限公司 | Picture method for pushing, mobile terminal and computer-readable medium |
| CN107977437A (en) * | 2017-12-06 | 2018-05-01 | 广东欧珀移动通信有限公司 | Image processing method, device, storage medium and electronic equipment |
| CN108038161A (en) * | 2017-12-06 | 2018-05-15 | 北京奇虎科技有限公司 | Information recommendation method, device and computing device based on photograph album |
Also Published As
| Publication number | Publication date |
|---|---|
| WO2020063014A1 (en) | 2020-04-02 |
| CN109508321A (en) | 2019-03-22 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109508321B (en) | Image display method and related product | |
| CN106528745B (en) | Method and device for recommending resources on mobile terminal and mobile terminal | |
| CN109389162B (en) | Sample image screening technique and device, electronic equipment and storage medium | |
| CN107729946B (en) | Picture classification method, device, terminal and storage medium | |
| CN108960283B (en) | Classification task increment processing method and device, electronic equipment and storage medium | |
| CN114816610B (en) | Page classification method, page classification device and terminal equipment | |
| CN111262777A (en) | Group message display method and electronic equipment | |
| CN105187692A (en) | Video recording method and device | |
| CN106022071A (en) | Fingerprint unlocking method and terminal | |
| CN110177040A (en) | Picture sharing method and mobile terminal | |
| CN103699373A (en) | Interface color display method, device and system | |
| CN113316778A (en) | Equipment recommendation method and related product | |
| CN108021470A (en) | A kind of data backup method, mobile terminal and computer-readable recording medium | |
| CN110933312A (en) | Photographing control method and related product | |
| CN110162264B (en) | Application processing method and related product | |
| CN108924439B (en) | Image processing method and related product | |
| CN110796673B (en) | Image segmentation method and related product | |
| CN109359453B (en) | Unlocking method and related product | |
| CN108462794A (en) | A kind of method for information display and mobile terminal | |
| CN108989555B (en) | Image processing method and related product | |
| CN108737657B (en) | Antenna control method and related product | |
| CN110753159B (en) | Incoming call processing method and related product | |
| CN115565236A (en) | Face recognition attack processing method, device, equipment and storage medium | |
| CN110611732B (en) | Window control method and related product | |
| CN114723987A (en) | Training method of image label classification network, image label classification method and device |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |