CN111798457B - Image visual weight determining method and device and image evaluation method - Google Patents
Image visual weight determining method and device and image evaluation method Download PDFInfo
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Abstract
The application discloses an image visual weight determining method. The image visual weight determining method comprises the steps of obtaining an image to be identified; dividing an image to be recognized into at least one grid according to an image division method, and marking the divided grids; acquiring selected segmentation grids and wave frequency recording information according to the grid visual weight test model; and determining the visual weight of the chart according to the recorded information. The method and the device solve the technical problem that the most impressive scheme cannot be selected from different advertisement schemes in the existing method.
Description
Technical Field
The present application relates to the field of image processing, and in particular, to a method and an apparatus for determining visual weight of an image, and an image evaluation method.
Background
Today, advertising is becoming more and more important to the promotion of products for merchants. How to select the most impressive scheme from different advertisement schemes is also a problem that merchants want to solve urgently.
Currently, for advertisement testing, qualitative interview is mainly used, and a quantitative questionnaire investigation or a small amount of eye tracker testing methods also exist. However, in either study approach, there are limitations and pain points that make it difficult to obtain complete and objective advertising test results. The evaluation method of the group interview is difficult to popularize in a large scale; the judgment of the host is excessively relied on; meanwhile, the method has the defects of high execution cost and long execution period. The disadvantage of the eye tracker test is that the execution cost is high, the sample size is difficult to scale, and the mass population is difficult to cover; and the system must be executed locally in an enterprise, and the execution difficulty across the ground is large. For the traditional questionnaire investigation, the limitations are high execution cost and long recovery period; special samples are difficult to obtain; moreover, only index information can be acquired, and the actual viewing experience of the subject is difficult to understand.
In order to quickly catch the attention of the viewer, the viewer is attracted; to make the advertisement/package look more comfortable, the viewer is willing to stay longer, absorb more information, and stay with the viewer; also in order to have critical information delivered at critical locations and to leave an impression in the viewer's memory, no effective solution has been proposed.
Disclosure of Invention
The main object of the present application is to provide an image visual weight determination method to solve the problem of selecting the most impressive scheme from different advertisement schemes.
In order to achieve the above object, the present application provides an image visual weight determination method, an image visual weight determination apparatus, and an image evaluation method.
In a first aspect, the present application provides a method for image visual weight determination.
The image visual weight determination method according to the application comprises the following steps:
acquiring an image to be identified;
dividing an image to be recognized into at least one region according to an image division method, and marking a division grid;
acquiring selected segmentation grids and wave frequency recording information according to the grid visual weight test model;
and determining the visual weight of the chart according to the recorded information.
Further, according to the graph visual weight test model, obtaining the selected segmentation graph and the wave frequency recording information comprises:
acquiring a watching time length and a trigger chart;
and determining the selected segmentation chart and the wave-time recording information according to the viewing time and the trigger chart.
Further, the formula of the visual weight of the graph grid is as follows:
m is the number of the segmentation grids; w is amIs the visual weight score of chart m; deltaIs the wave number using the test model; x is the number ofmδFor selecting the frequency of the grid m at the order of delta waves, NδThe total number of people who participated in the grid selection at delta wave times; r isδIs an attractive force coefficient at the order of delta waves, and 1 > rδ＞0。
In a second aspect, the present application provides an image evaluation method, including:
acquiring an image to be identified;
determining the visual weight of different grids based on the image visual weight determination method provided by the first aspect;
and determining an evaluation index according to the visual weight of the chart.
Further, evaluation indices include, but are not limited to: an index of eye-sucking, an index of information, an index of overall attractiveness.
Further, the formula of the eye-sucking index is as follows:
HI＝max(wm)
the formula of the information index is:
the overall attractiveness index is formulated as:
GAI＝HI×MI
HI is the suction index, wmIs a visual weight score of the segmented region m, MI is an information index,
segmenting the grid m for the keykThe visual weight score of (a), GAI, is the overall appeal index.
In a third aspect, the present application provides an image visual weight determination apparatus.
The image visual weight determination apparatus according to the present application includes:
an image acquisition module: acquiring an image to be identified;
an image segmentation module: dividing an image to be recognized into at least one grid according to an image division method, and marking the divided grids;
an information acquisition module: acquiring selected segmentation grids and wave frequency recording information according to the grid visual weight test model;
a result output module: and determining the visual weight of the chart according to the recorded information.
In a fourth aspect, the present application provides an electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the steps of the image visual weight determination method provided in the first aspect and/or the image evaluation method provided in the second aspect when executing the program.
In a fifth aspect, the present application provides a non-transitory computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of the image visual weight determination method provided by the first aspect and/or the image evaluation method provided by the second aspect.
In the embodiment of the application, the purpose of evaluating the image is achieved by adopting an image segmentation mode and a chart visual weight test model, so that the method for determining the visual weight of the image is realized, and the problem of selecting the most impressive scheme from different advertisement schemes is solved.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this application, serve to provide a further understanding of the application and to enable other features, objects, and advantages of the application to be more apparent. The drawings and their description illustrate the embodiments of the invention and do not limit it. In the drawings:
FIG. 1 is a schematic flow chart diagram of a method for determining visual weight of an image according to an embodiment of the present application;
FIG. 2 is a schematic diagram of original advertising scheme segmentation according to an embodiment of the present application;
FIG. 3 is a schematic flow chart of image evaluation according to an embodiment of the present application;
fig. 4 is a schematic flow chart of an image visual weight determining apparatus according to an embodiment of the present application.
Detailed Description
In order to make the technical solutions better understood by those skilled in the art, 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 partial embodiments of the present application, but not all 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.
It should be noted that the terms "first," "second," and the like in the description and claims of this application and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It should be understood that the data so used may be interchanged under appropriate circumstances such that embodiments of the application described herein may be used. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
Moreover, some of the above terms may be used to indicate other meanings besides the orientation or positional relationship, for example, the term "on" may also be used to indicate some kind of attachment or connection relationship in some cases. The specific meanings of these terms in the present invention can be understood by those skilled in the art as appropriate.
It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict. The present application will be described in detail below with reference to the embodiments with reference to the attached drawings.
As mentioned by way of background, merchants will pass a visual weight test on advertisements in order to leave an impression in the viewer's mind. The visual weight of an image is the ability of the image picture elements to attract the visual attention of a viewer. The stronger the ability, the higher the score, meaning the heavier the visual weight; conversely, the weaker the ability, the lower the score, the lighter the visual weight. Aiming at the problem of how to calculate the visual weight of an image, the invention provides an image visual weight determining method, which comprises the following steps:
as shown in fig. 1, the method includes steps S1 to S4 as follows:
s1: acquiring an image to be identified;
s2: dividing an image to be recognized into at least one grid according to an image division method, and marking the divided grids;
illustratively, as shown in fig. 2, the embodiment of the present invention divides the picture elements in a grid form, and is denoted by the numbers 1, 2, 3, …, 15.
Further, according to the graph visual weight test model, obtaining the selected segmentation graph and the wave frequency recording information comprises:
s21: acquiring a watching time length and a trigger chart;
specifically, the preferred embodiment of the present invention can acquire δ acquisition viewing durations for viewing δ times and δ trigger grid cells for triggering.
Specifically, the preferred embodiment of the present invention may also obtain the first viewing time and at least two triggered grid cells.
Further, delta is more than or equal to 2.
In an example, the embodiment of the present invention obtains the durations 1s, 2s, and 3s of 3 views, and processes the first triggered grid pattern, the second triggered grid pattern, and the third triggered grid pattern.
In an example, the embodiment of the present invention obtains the time length of viewing for 1 time by 5s, and processes the first triggered grid pattern, the second triggered grid pattern, and the third triggered grid pattern.
S22: and determining the selected segmentation chart and the wave-time recording information according to the viewing time and the trigger chart.
S3: and acquiring the selected segmentation chart and the wave frequency recording information according to the chart vision weight test model.
Further, the formula of the visual weight of the graph grid is as follows:
m is the number of the division cells, and m>1；wmIs the visual weight score of chart m; delta is the wave number of the used test model, and delta is more than or equal to 2; x is the number ofmδFor selecting the frequency of the grid m at the order of delta waves, NδThe total number of people who participated in the grid selection at delta wave times; r isδIs an attractive force coefficient at the order of delta waves, and 1 > rδ＞0。
By way of example, in the present exemplary embodiment, m is 15, δ is 3, Nδ＝1000，r1＝0.5，r2＝0.3， r3＝0.2。
S4: and determining the visual weight of the chart according to the recorded information.
Further, the grid visual weights include, but are not limited to: intuitive weight w of the gridmImage visual weight thermodynamic diagrams.
From the above description, it can be seen that the present invention achieves the technical effect of calculating the visual weight of an image.
It should be noted that the steps illustrated in the flowcharts of the figures may be performed in a computer system such as a set of computer-executable instructions and that, although a logical order is illustrated in the flowcharts, in some cases, the steps illustrated or described may be performed in an order different than presented herein.
According to an embodiment of the present invention, there is provided an image evaluation method, as shown in fig. 3, including:
s5: and acquiring an image to be identified.
S6: and determining the visual weight of the different grids based on the image visual weight determination method.
S7: and determining an evaluation index according to the visual weight of the chart.
Further, evaluation indices include, but are not limited to: an index of eye-sucking, an index of information, an index of overall attractiveness.
Further, the formula of the eye-sucking index is as follows: HI ═ max (w)m)
The formula of the information index is:
the overall attractiveness index is formulated as: GAI ═ HI × MI
HI is the suction index, wmA visual weight score for the segmented frame m, MI is an information index,
segmenting the grid m for the keykThe visual weight score of (a), GAI, is the overall appeal index.
The key division grid refers to the transmission of information related to keys on the grid. Due to different information transmission tasks of different plane advertisements/packages, the distribution of key information on the picture is different. The number and location of the key segmentation grids will vary from project to project, in principle specified by the customer.
From the above description, it can be seen that the present invention achieves the following technical effects:
scale statistics of statistical value: the large base group can be easily covered in a short time, and the research result is more representative by the sample selection method of layered random sampling;
the cost is controllable: the network research reduces the loss of materials such as manual operation, instruments, paper and the like to the maximum extent, and greatly saves the research cost;
convenient statistics: the method has the advantages of simple execution, flexible region, synchronous network research in the region required by the client, easy operation, low technical barrier, capability of receiving the research by common netizens, and flexible adjustment of research form according to the client requirement.
It should be noted that the steps illustrated in the flowcharts of the figures may be performed in a computer system such as a set of computer-executable instructions and that, although a logical order is illustrated in the flowcharts, in some cases, the steps illustrated or described may be performed in an order different than presented herein.
According to an embodiment of the present invention, there is also provided an apparatus for implementing the image visual determination method, as shown in fig. 4, the apparatus including:
the image acquisition module 11: acquiring an image to be identified;
the image segmentation module 12: dividing an image to be recognized into at least one grid according to an image division method, and marking the divided grids;
the information acquisition module 13: acquiring selected segmentation grids and wave frequency recording information according to the grid visual weight test model;
the result output module 14: and determining a chart visual weight score according to the recorded information.
Further, according to the graph visual weight test model, obtaining the selected segmentation graph and the wave frequency recording information comprises:
acquiring a watching time length and a trigger chart;
and determining the selected segmentation chart and the wave-time recording information according to the viewing time and the trigger chart.
Further, the formula of the visual weight of the graph grid is as follows:
m is the number of the division cells, and m>1；wmIs the visual weight score of chart m; delta is the wave number of the used test model, and delta is more than or equal to 2; x is the number ofmδFor selecting the frequency of the grid m at the order of delta waves, NδThe total number of people who participated in the grid selection at delta wave times; r isδIs an attractive force coefficient at the order of delta waves, and 1 > rδ＞0。
It will be apparent to those skilled in the art that the modules or steps of the present invention described above may be implemented by a general purpose computing device, they may be centralized on a single computing device or distributed across a network of multiple computing devices, and they may alternatively be implemented by program code executable by a computing device, such that they may be stored in a storage device and executed by a computing device, or fabricated separately as individual integrated circuit modules, or fabricated as a single integrated circuit module from multiple modules or steps. Thus, the present invention is not limited to any specific combination of hardware and software.
The above description is only a preferred embodiment of the present application and is not intended to limit the present application, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present application shall be included in the protection scope of the present application.
Claims (5)
1. An image visual weight determination method, comprising:
acquiring an image to be identified;
dividing the image to be identified into at least one grid according to an image division method, and marking the divided grids;
acquiring selected segmentation grids and wave frequency recording information according to the grid visual weight test model;
determining the visual weight of the chart according to the recorded information;
the obtaining of the selected segmentation chart and the wave number recording information according to the chart vision weight test model comprises the following steps:
acquiring a watching time length and a trigger chart;
determining the selected segmentation chart and the wave frequency recording information according to the watching time length and the trigger chart;
the formula of the graph visual weight is as follows:
m is the number of the segmentation grids;
is shown as a drawingA visual weight score of grid; δ is the wave number using the test model;
to select the frequency of bin m at delta-wave order,
the total number of people who participated in the grid selection at delta wave times;
is the coefficient of attraction at the delta wave order, and
。
2. an image evaluation method, comprising:
acquiring an image to be identified;
determining a grid visual weight for different grids based on the image visual weight determination method of claim 1;
determining an evaluation index according to the graph visual weight;
the evaluation index includes: an eye-sucking index, an information index, a total attraction index;
the formula of the eye suction index is as follows:
the formula of the information index is as follows:
the overall attractiveness index is formulated as:
HI is the index of the eye-sucking,
a visual weight score for the segmented frame m, MI is an information index,
segmenting lattices for keys
The visual weight score of (a), GAI is the overall appeal index;
wherein, the key segmentation chart grid
Refers to the transmission of information about the keys on the grid.
3. An image visual weight determining apparatus, comprising:
an image acquisition module: acquiring an image to be identified;
an image segmentation module: dividing the image to be identified into at least one grid according to an image division method, and marking the divided grids;
an information acquisition module: according to the graph visual weight test model, obtaining the selected segmentation graph and the wave frequency recording information, and specifically comprising the following steps: acquiring a watching time length and a trigger chart; determining the selected segmentation chart and the wave frequency recording information according to the watching time length and the trigger chart;
a result output module: determining the visual weight of the chart according to the recorded information;
the formula of the graph visual weight is as follows:
m is the number of the segmentation grids;
is the visual weight score of chart m; δ is the wave number using the test model;
to select the frequency of bin m at delta-wave order,
the total number of people who participated in the grid selection at delta wave times;
is the coefficient of attraction at the delta wave order, and
。
4. a computer-readable storage medium storing computer instructions for causing a computer to execute the image visual weight determination method of claim 1 and/or the image evaluation method of claim 2.
5. An electronic device, comprising: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores a computer program executable by the at least one processor, the computer program being executable by the at least one processor to cause the at least one processor to perform the image visual weight determination method of claim 1 and/or the image evaluation method of claim 2.
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| CN110658907A (en) * | 2018-06-28 | 2020-01-07 | 阿里健康信息技术有限公司 | Method and device for acquiring user behavior data |
| CN109919065A (en) * | 2019-02-26 | 2019-06-21 | 浪潮金融信息技术有限公司 | A method of focus is obtained on the screen using eyeball tracking technology |
| CN111079740A (en) * | 2019-12-02 | 2020-04-28 | 咪咕文化科技有限公司 | Image quality evaluation method, electronic device, and computer-readable storage medium |
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