Advertisement identification pattern generation method and device, electronic equipment and storage medium

Abstract

The invention provides an advertisement identification pattern generation method and device, electronic equipment and a storage medium, and relates to the technical field of computer aided design, and the method comprises the steps: obtaining user demand data, screening out images meeting requirements from an image library, forming a first image set, extracting the feature parameters of the image set, and generating an advertisement identification pattern; calculating a readability index, a color specification degree index and a negative space balance index based on the characteristic parameters, comprehensively generating an image quality evaluation index, correcting the image quality evaluation index through a length-width ratio and shape complexity according to the characteristics of the images in the first image set to obtain a correction value, and sorting the correction value according to the priority to obtain a second image set; and finally selecting the image with the maximum image quality evaluation index correction value as the optimal selection of the user. According to the method, high-quality design of the advertisement identification is realized through feature extraction and prioritization correction.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of computer-aided design, in particular to an advertisement logo generation method and device, electronic equipment and storage medium. BACKGROUND

[0002] Current pattern generation methods mainly involve the cross-application of computer graphics, artificial intelligence, machine learning, and visual perception. Traditional advertisement logo design relies on the experience and creativity of human designers, but with the advancement of digital technology, algorithm-based pattern generation has gradually become a trend. Using computerized analysis, image recognition, and feature extraction techniques, efficient advertisement logo design can be achieved, improving the quality and efficiency of design. In addition, intelligent design methods can automatically generate advertisement patterns that meet design requirements based on visual saliency, color matching, readability, and negative space parameters.

[0003] Currently, the development of advertisement logo generation technology is gradually moving towards intelligence, individualization, and automation. By building a multi-modal material database and combining deep learning technology, advertisement design can not only be automatically optimized according to user input requirements, but also can be creatively innovative and recommended based on historical data. Especially in the context of the continuous progress of big data analysis and AI algorithms, designers can use AI-assisted tools to more accurately match suitable materials, reduce design time costs, and improve design consistency and creative performance.

[0004] In the prior art, traditional advertisement design often relies on experience and human creativity, resulting in low design efficiency, unstable quality, and difficulty in systematically evaluating the visual effects and practicality of the design. Moreover, traditional advertisement logo generation often lacks flexibility in design, making it difficult to make personalized adjustments according to user-specific needs, resulting in generated patterns that cannot fully meet the diverse needs of the market.

[0005] Therefore, it is necessary to provide an advertisement logo generation method, device, electronic equipment, and storage medium to solve the above problems.

[0006] The above information disclosed in the background section is only used to strengthen the understanding of the background of the present disclosure, and therefore it can include information that does not constitute prior art known to those of ordinary skill in the art. SUMMARY

[0007] The present application aims to provide an advertisement logo generation method, device, electronic equipment, and storage medium to solve the problems raised in the background.

[0008] To achieve the above-mentioned purpose, the present application provides the following technical solutions:

[0009] An advertisement logo generation method, the specific steps include:

[0010] Step 1: Obtain the design requirement data of the user, the design requirement data includes the aspect ratio of the logo, the color combination, the graphic element and the application scene category, select multiple images from the image library that meet the design requirement data to obtain a first image set;

[0011] Step 2: Obtain the feature parameters of the images in the first image set, the feature parameters include the length-width ratio of the image, the shape complexity, the color coordination, the color focus degree, the blank area ratio and the negative space distribution uniformity;

[0012] Step 3: Calculate the related feature indexes for representing the image quality according to the extracted feature parameters, the feature indexes include the readability index, the color specification index and the negative space balance index, and generate an image quality evaluation index by comprehensively using the readability index, the color specification index and the negative space balance index;

[0013] Step 4: Correct and adjust the image quality evaluation index based on the length-width ratio and the shape complexity of the images in the first image set to obtain an image quality evaluation index correction value, sort the image quality evaluation index correction values according to the priority size order, and take the one with the largest image quality evaluation index correction value as the optimal selection of the user.

[0014] Further, the method for obtaining the feature parameters of the images in the first image set is as follows:

[0015] Use the edge detection and contour analysis in computer comprehensive feature extraction technology to detect the patterns of the images in the first image set, identify the outer contour of the image, obtain the length and width of the image, and the ratio of the length to the width is the length-width ratio ; Obtain the perimeter and area of the image, and the ratio of the perimeter to the area is the shape complexity ;

[0016] Based on the dominant color extraction technology in computer comprehensive feature extraction technology, use clustering algorithm to extract the dominant color tone, obtain the dominant color tone of each image, and through color space conversion technology, obtain the brightness and color component of the image color, and calculate the color coordination and color focus degree, the formula is as follows:

[0017]

[0018]

[0019]

[0020]

[0021] wherein, , respectively represent the color harmony and color focus of the image, is the index of the dominant color tone in the image, and , is the total number of the extracted dominant color tones in the image, , , is the third component in the Lab color space of the first dominant color tone, , , is the third component in the Lab color space of the first other color, is the index of the other color, and , is the proportion of the first dominant color tone in the image, is the maximum color harmony between the dominant color tone and the other color, is the color harmony of the first dominant color tone in the image;

[0022] Based on the layout data of the images in the first image set, the images are evenly divided into grids by using target detection and segmentation techniques in computer synthesis feature extraction technology, and then the total area of the images and the area of the negative space are obtained to calculate the blank area ratio and the negative space distribution uniformity, according to the formula:

[0023]

[0024]

[0025] wherein, , respectively represent the blank area ratio and the negative space distribution uniformity, , respectively are the total area of the negative space and the total area of the image, represents the area of each grid, is the total number of the divided grids, is the index of the grid number, , is the area of the negative space in the first grid.

[0026] Further, the related feature indexes for representing the quality of the advertising logo pattern are calculated and generated according to the extracted feature parameters of the related design requirements, and the method is as follows:

[0027] The readability index of the advertising logo pattern is calculated based on the blank area ratio, color coordination degree and color focusing degree of the advertising logo pattern, and the formula is:

[0028]

[0029] wherein, represents the readability index of the advertising logo pattern;

[0030] The color specification index of the advertising logo pattern is calculated based on the color coordination degree and color focusing degree of the advertising logo pattern, and the formula is:

[0031]

[0032] wherein, represents the color specification index of the advertising logo pattern, is an ideal color coordination degree, is an ideal color focusing degree;

[0033] The negative space balance index of the advertising logo pattern is calculated based on the blank area ratio and negative space distribution uniformity of the advertising logo pattern, and the formula is:

[0034]

[0035] wherein, represents the negative space balance index of the advertising logo pattern.

[0036] Further, the image quality evaluation index is generated by comprehensively using the readability index, the color specification index and the negative space balance index, and the formula is:

[0037]

[0038] wherein, represents the image quality evaluation index.

[0039] Further, the image quality evaluation index is corrected and adjusted based on the length-width ratio and shape complexity of the images in the first image set to obtain an image quality evaluation index correction value, and the image quality evaluation index correction values are sorted according to the priority size order, and the image with the largest image quality evaluation index correction value is selected as the optimal selection of the user, and the method is:

[0040] According to the different style requirements of the user for the logo pattern and in combination with the length-width ratio and shape complexity of the images in the first image set, the image quality evaluation index is corrected, and the formula is:

[0041]

[0042] wherein, an image quality evaluation index correction value, is a correction factor, is a shape complexity of an image in the first image set, is an aspect ratio of an image in the first image set, is an ideal aspect ratio;

[0043] The image quality evaluation index correction value is prioritized according to the shape complexity of the style required by the user, the optimal selection of the user is obtained, and the feature parameters of the image in the first image set corresponding to the optimal selection are used as the design parameters of the advertising logo pattern for design.

[0044] The application also provides an advertising logo pattern generation device, which is used for executing the above-mentioned advertising logo pattern generation method, and comprises:

[0045] A design requirement analysis module is configured to obtain design requirement data of a user, the design requirement data comprising an aspect ratio, a color combination, a graphic element and an application scenario category of a logo pattern, filter a plurality of images meeting the design requirement data from an image library to obtain a first image set;

[0046] A feature extraction and generation module is configured to obtain feature parameters of images in the first image set, the feature parameters comprising an aspect ratio, a shape complexity, a color coordination degree, a color focus degree, a blank area ratio and a negative space distribution uniformity of the images;

[0047] An image quality evaluation index calculation module is configured to calculate relevant feature indexes for representing image quality according to the extracted feature parameters, the feature indexes comprising a readability index, a color specification index and a negative space balance index, and generate an image quality evaluation index by comprehensively using the readability index, the color specification index and the negative space balance index;

[0048] A calibration correction and optimal pattern selection module is configured to correct and adjust the image quality evaluation index based on the aspect ratio and the shape complexity of the images in the first image set to obtain an image quality evaluation index correction value, and sort the image quality evaluation index correction values according to priority size order, and take the image quality evaluation index correction value with the maximum value as the optimal selection of the user.

[0049] The application also provides an electronic device, which comprises:

[0050] a processor; and a memory connected with the processor in communication; wherein the memory stores instructions executable by the processor, and the instructions are executed by the processor to make the processor execute the above-mentioned advertising logo pattern generation method.

[0051] The application further provides a non-transitory computer-readable storage medium storing computer instructions for causing a computer to execute the above-mentioned advertisement logo generation method.

[0052] Compared with the prior art, the application has the following beneficial effects:

[0053] The application significantly improves the efficiency and quality of advertisement logo design. Through comprehensive feature extraction technology, it can quickly analyze user needs and extract key parameters, thereby reducing the time and effort required for manual design. At the same time, based on the evaluation of quantitative feature index, it ensures that the generated design reaches a higher standard in visual effect and practicality. In addition, the personalized design capability is enhanced, making the advertisement logo better fit the diversified market demand, improving the emotional resonance with the target audience, and thus improving the attractiveness and propagation effect of the advertisement;

[0054] The advertisement logo generation method of the application accurately obtains user design requirement data, combines multi-dimensional feature parameters such as aspect ratio, shape complexity, and color coordination, and comprehensively generates an image quality evaluation index. Such design not only meets the user's individual needs, but also ensures the visual appeal of the logo and the clarity of information transmission, improving user satisfaction and advertisement propagation effect.

[0055] In addition, the system uses advanced computer vision technology for feature extraction, making image analysis more scientific and efficient. By correcting and prioritizing the image quality evaluation index, the optimal choice is quickly identified, ensuring the professionalism and optimization effect of the design. This method is not only suitable for various application scenarios, but also has good market promotion potential, helping to improve the efficiency and quality of designers' works.

[0056] The application realizes efficient and personalized advertisement logo design through comprehensive computer feature extraction technology and quantitative evaluation system, improves design quality and ensures copyright compliance. BRIEF DESCRIPTION OF DRAWINGS

[0057] Figure 1 The figure is a schematic diagram of the overall method flow of the application.

[0058] Figure 2 The figure is a schematic diagram of the system module flow of the application. DETAILED DESCRIPTION

[0059] In order to make the purpose, technical scheme and advantages of the application clearer, the application is further described in detail below in combination with specific embodiments.

[0060] Unless otherwise defined, technical terms or scientific terms used in the present application shall have the same meaning as those commonly understood by one of ordinary skill in the art to which the present application belongs. The terms "first", "second", and similar terms are used herein merely to distinguish one element from another, and are not intended to imply any order or sequence. The terms "include", "contain", and similar terms are intended to cover a non-exclusive inclusion, such that processes, methods, articles, or apparatuses that consist of, include, or contain the recited elements, or their equivalents, are within the scope of the present application. The terms "connect" and "couple" and similar terms are not limited to a direct connection or coupling, but also include an indirect connection or coupling, such as through an intermediary. The terms "upper", "lower", "left", "right", and the like are used only to indicate relative positions, and can change accordingly when the absolute positions of the described objects change.

[0061] Embodiments:

[0062] Referring to Figure 1 The present application provides a technical solution:

[0063] An advertisement logo generation method, the specific steps comprising:

[0064] Step 1: Obtain the design requirement data of the user, the design requirement data including the aspect ratio, color combination, graphic element and application scenario category of the logo, filter a plurality of images from the image library that meet the design requirement data to obtain a first image set;

[0065] Step 2: Obtain the feature parameters of the images in the first image set, the feature parameters including the aspect ratio, shape complexity, color coordination, color focus, blank area ratio and negative space distribution uniformity of the images;

[0066] Step 3: Calculate the related feature indexes for representing the image quality according to the extracted feature parameters, the feature indexes including the readability index, color specification index and negative space balance index, and generate an image quality evaluation index by comprehensively using the readability index, color specification index and negative space balance index;

[0067] Step 4: Correct and adjust the image quality evaluation index based on the aspect ratio and shape complexity of the images in the first image set to obtain an image quality evaluation index correction value, sort the image quality evaluation index correction values according to the priority size order, and take the one with the largest image quality evaluation index correction value as the optimal selection of the user.

[0068] It should be noted that when designing logos, the aspect ratio, color combination, graphic elements, and application scenario category can be determined within a specific range based on actual needs. The following are suggested ranges: the aspect ratio of the logo can be designed according to different pattern shapes. For example, the aspect ratio of a classic rectangular pattern can be set to 3:2 or 2:1. The basic colors that can be selected in the color combination include red, blue, yellow, green, black, and white. It is recommended not to exceed 4-5 main colors to maintain visual unity. Graphic elements should cover geometric shapes, natural elements, abstract elements, and text elements. The application scenario category is limited to a single commercial scenario.

[0069] It should be noted that by comprehensively applying computer vision technology and deeply analyzing the geometric features, color characteristics, and layout of images, the generated logos are ensured to have high-quality visual performance and effective information delivery. By accurately calculating key parameters such as aspect ratio, shape complexity, color harmony, and negative space distribution, designers can better understand user needs, optimize design solutions, thereby improving the recognizability and attractiveness of advertising logos, and ultimately enhancing brand communication effectiveness and market competitiveness.

[0070] Therefore, it is necessary to obtain the feature parameters of the images in the first image set, and the method used is as follows:

[0071] Edge detection and contour analysis, techniques from computer-aided feature extraction, are used to perform pattern detection on the images in the first image set. This identifies the outer contours of the images and obtains their length and width. The ratio of the image's length to its width is the aspect ratio. Obtain the perimeter and area of ​​the image; the ratio of the perimeter to the area is the shape complexity. ;

[0072] Based on the dominant color extraction technique in computer-integrated feature extraction technology, a clustering algorithm is used to extract the dominant color tone of each image. Then, color space conversion technology is used to obtain the brightness and color components of the image colors. Color harmony and color focus are calculated using the following formulas:

[0073]

[0074]

[0075]

[0076]

[0077] in, , These represent the color harmony and color focus of the image, respectively. The index of the dominant color tone in the image, and , the total number of dominant colors extracted in the image, 、 、 is the third component in the Lab color space of the first dominant color, 、 、 is the third component in the Lab color space of the first other color, is the index of the other color, and , is the proportion of the first dominant color in the image, is the maximum color harmony between the dominant color and the other color, is the color harmony of the first dominant color in the image;

[0078] In the above formula for calculating the color harmony of the image, the smaller the better, because the value reflects the visual difference between colors, and the smaller the visual difference means that they are more visually harmonious and unified, and these colors can better blend together to form a harmonious visual effect; in the above formula for calculating the color focus of the image, the larger the better, because the value of indicates the concentration and harmony of the dominant color in the image, and a higher means that the dominant color occupies a larger proportion in the image and has smaller differences between each other. This situation will make some colors more visually prominent, thereby attracting the attention of the audience, making the image more attractive.

[0079] Based on the layout data of the images in the first image set, using target detection and segmentation techniques in computer feature extraction technology, the image is evenly divided into grid, and then the total area and negative space area of the image are obtained to calculate the blank area ratio and negative space distribution uniformity, according to the formula:

[0080]

[0081]

[0082] wherein, , respectively represent the blank area ratio and the negative space distribution uniformity, , respectively the total area of the negative space, the total area of the image, represents the area of each grid, to divide the total number of grids, to index the number of grids, , to the area of the negative space in the first grid; in the above formula, The smaller the better, a smaller white area ratio means that the negative space in the image is relatively small compared to the total area of the image, which can make the image more compact and concise, thus reducing the visual clutter and helping the audience focus more easily on the main content or information; The larger the better, because a larger value indicates that the negative space is more evenly distributed in the image. This uniformity helps to achieve visual balance, making each part of the image look more coordinated, and uniform negative space can avoid the feeling of visual congestion, making the audience's eyes flow more comfortably throughout the image.

[0083] It should be noted that in advertising design, it is crucial to evaluate the quality of the advertising logo pattern, therefore, based on the calculation of relevant feature parameters such as white area ratio, color coordination, color focus, color coordination, color focus and negative space distribution uniformity, readability index, color specification index and negative space balance index can be generated, which can effectively quantify the visual effect and information transmission capacity of the design work. These indexes not only help designers optimize the composition and color use of advertising logos, but also ensure their effectiveness in attracting the attention of target audiences and improving brand recognition. Through these quantitative indicators, designers can more scientifically evaluate and adjust design schemes, thus creating high-quality advertising works that meet market demand.

[0084] Therefore, it is necessary to calculate the relevant feature index for representing the quality of the advertising logo pattern according to the extracted relevant design requirement feature parameters, and the method is as follows:

[0085] Based on the white area ratio, color coordination and color focus of the advertising logo pattern, the readability index of the advertising logo pattern is calculated, and the formula is as follows:

[0086]

[0087] wherein, represents the readability index of the advertising logo pattern; in the above formula, The smaller the better, the larger the better, which means that a smaller white area ratio indicates that the negative space in the advertising logo pattern is relatively small, and the design is more compact. This situation usually makes it easier for the audience to read and understand the information in the pattern, thus improving the overall readability; increases, Increased contrast means that due to the increased color focus, the visual difference between text and background becomes more pronounced, making it easier for viewers to distinguish the text content. This high contrast helps improve the readability of information, allowing viewers to identify and understand the information in advertising signage more quickly; Decrease Enlarging the color difference means that smaller color variations can create a smoother overall visual experience, making viewers less likely to be distracted by jarring color contrasts. This design makes it easier to guide the viewer's eye and focus their attention on the core message of the advertisement.

[0088] The color standardization index of advertising logos is calculated based on the color coordination and color focus of the logo design. The formula used is as follows:

[0089]

[0090] in, The color standardization index represents the color accuracy of advertising logo designs. For ideal color coordination, For ideal color focus; in the above formula, Decrease This will increase the color intensity, which means that the colors in the advertising logo become more consistent and unified. This consistency in colors helps to improve visual harmony, allowing the advertising logo to convey a professional and reliable image to the audience. Increase, This increased clarity means enhanced color contrast in advertising signage, allowing viewers to more clearly distinguish text and images. The information presented in the signage becomes more explicit, reducing visual blur. This improved clarity enables viewers to grasp the core message of the advertisement quickly, enhancing the efficiency of information delivery.

[0091] The negative space balance index of an advertising sign is calculated based on the ratio of blank area to negative space distribution uniformity. The formula used is as follows:

[0092]

[0093] in, The negative space balance index represents the design of the advertising logo; in the above formula, The larger, The larger, the better The value indicates a more even distribution of negative space, giving the advertising signage a greater sense of space. This sense of space helps to avoid visual crowding and clutter, thereby enhancing the overall aesthetics of the design and improving the viewer's visual experience. Decrease Increasing means that the proportion of white space or negative space in the design is relatively reduced, and in the case of negative space reduction, the main graphics and text can be better highlighted.

[0094] It should be noted that the image quality evaluation index combines the readability index, the color specification index and the negative space balance index, providing a comprehensive quality evaluation tool for advertising design. By integrating these three key factors, designers can optimize the advertising logo, improve the clarity and visual appeal of information transmission, enhance user experience, and thus enhance the brand's awareness and influence in the highly competitive market.

[0095] Therefore, it is necessary to generate an image quality evaluation index by comprehensively using the readability index, the color specification index and the negative space balance index, and the formula is as follows:

[0096]

[0097] Among them, The image quality evaluation index; in the above formula, The greater the better, The greater the value of the image quality evaluation index, the higher the overall quality of the image, and the greater the The higher the readability index makes it easier for the audience to understand the information, and also indicates that the color specification index is better, enhancing the visual appeal, while the optimization of the negative space balance index makes the design more balanced and harmonious, which comprehensively reflects the professionalism and appeal of the image design, helping to improve the brand image and advertising effect.

[0098] It should be noted that different style requirements of users for logo patterns include two types of simple fashion and gorgeous splendor, and different style requirements will result in different image quality evaluation indexes, that is, different characteristic parameters as design parameters, so it is necessary to correct the image quality evaluation index according to the style requirements of the user. This process not only optimizes the design to meet the specific needs of the user and improve user satisfaction, but also enhances the visual appeal and information transmission effect of the image. Through data-driven correction calculation, design decisions become more scientific and systematic, reducing subjective bias. In addition, according to the priority ranking of the corrected image quality evaluation index, it is helpful to quickly identify the optimal choice, improve work efficiency, and ensure the consistency of brand visual expression, thereby improving brand recognition and market competitiveness.

[0099] Therefore, it is necessary to correct and adjust the image quality evaluation index based on the aspect ratio and shape complexity of the images in the first image set, to obtain an image quality evaluation index correction value, and to sort the image quality evaluation index correction values in order of priority, so that the image with the largest image quality evaluation index correction value is the optimal choice for the user.

[0100] According to the different style requirements of the user for the identification pattern and in combination with the aspect ratio and shape complexity of the images in the first image set, the image quality evaluation index is corrected, and the formula is:

[0101]

[0102] wherein, represents the image quality evaluation index correction value, is a correction factor, is the shape complexity of the images in the first image set, is the aspect ratio of the images in the first image set, is an ideal aspect ratio;

[0103] According to the shape complexity of the user's desired style, the image quality evaluation index correction values are sorted in order of priority, to obtain the optimal choice for the user, and the feature parameters of the image in the first image set corresponding to the optimal choice are used as the design parameters of the advertising identification pattern.

[0104] In the above correction formula, the correction calculation logic is: This is the absolute difference between the actual aspect ratio and the ideal aspect ratio. The smaller the difference, the closer the aspect ratio of the image is to the user's requirements, and vice versa. This factor plays a "punishment" role in the formula, preventing the image quality evaluation index from being too high due to a large aspect ratio deviation, and the shape complexity reflects the design complexity of the image. More complex images are more attractive, the value will be larger, and simple images, the value will be smaller. The shape complexity has a positive correction effect on the corrected image quality.

[0105] According to the shape complexity of the user's desired style, the image quality evaluation index correction values are sorted in order of priority, to obtain the optimal choice for the user, and the feature parameters of the image in the first image set corresponding to the optimal choice are used as the design parameters of the advertising identification pattern. If the user's desired style is a fashion minimalist style, then the are arranged in ascending order, and the smallest one is selected as the optimal choice. If the user's desired style is a luxurious and splendid style, then the Arrange in descending order and select the largest one As the best choice.

[0106] Please refer to Figure 2 The application further provides an advertisement logo pattern generation device for executing the above-mentioned advertisement logo pattern generation method, comprising:

[0107] A design requirement analysis module is configured to obtain design requirement data of a user, the design requirement data including an aspect ratio, color combination, graphic element, and application scenario category of a logo pattern, filter a plurality of images from an image library that meet the design requirement data to obtain a first image set;

[0108] A feature extraction generation module is configured to obtain feature parameters of images in the first image set, the feature parameters including an aspect ratio, shape complexity, color coordination, color focus, blank area ratio, and negative space distribution uniformity of the images;

[0109] An image quality evaluation index calculation module is configured to calculate relevant feature indexes for representing image quality according to the extracted feature parameters, the feature indexes including a readability index, a color specification index, and a negative space balance index, and generate an image quality evaluation index by comprehensively using the readability index, the color specification index, and the negative space balance index;

[0110] A calibration correction and optimal pattern selection module is configured to correct and adjust the image quality evaluation index based on the aspect ratio and shape complexity of the images in the first image set to obtain an image quality evaluation index correction value, sort the image quality evaluation index correction values according to priority size order, and select the image with the largest image quality evaluation index correction value as the optimal choice of the user.

[0111] The application further provides an electronic device, comprising:

[0112] a processor; and a memory connected to the processor in communication; wherein the memory stores instructions executable by the processor, and the instructions are executed by the processor to cause the processor to execute the above-mentioned advertisement logo pattern generation method.

[0113] The application further provides a non-transitory computer readable storage medium storing computer instructions for causing a computer to execute the above-mentioned advertisement logo pattern generation method.

[0114] The above formulas are all dimensionless values calculated, the formula is obtained by collecting a large amount of data to simulate the most recent real situation, and the preset parameters in the formula are set by a person skilled in the art according to the actual situation.

[0115] The above embodiments can be implemented wholly or partially by software, hardware, firmware or any other combination. When implemented by software, the above embodiments can be implemented wholly or partially in the form of a computer program product. Those skilled in the art can realize that the units and algorithm steps of the examples described in connection with the embodiments disclosed herein can be realized by electronic hardware or a combination of computer software and electronic hardware. Whether the functions are executed by hardware or software methods depends on the specific application and design constraints of the technical solutions.

[0116] The units described as separate components can or can not be physically separated, and the components shown as units can or can not be physical units, which can be located in one place or distributed on multiple network units. Part or all of the units can be selected to achieve the purpose of the embodiments according to actual needs.

[0117] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto, any person skilled in the art can easily think of changes or replacements within the technical range disclosed in the present application, which should be covered within the protection scope of the present application.

Claims

1. A method for generating an advertising logo pattern, characterized in that, The specific steps include: Step 1: Obtain the user's design requirement data, which includes the aspect ratio, color combination, graphic elements, and application scenario category of the logo pattern. Select multiple images from the image library that meet the design requirement data to obtain the first image set. Step 2: Obtain the feature parameters of the images in the first image set. The feature parameters include the aspect ratio, shape complexity, color harmony, color focus, blank area ratio, and negative space distribution uniformity of the images. Step 3: Calculate and generate relevant feature indices to characterize image quality based on the extracted feature parameters. The feature indices include readability index, color normalization index, and negative space balance index. Then, use the readability index, color normalization index, and negative space balance index to generate an image quality evaluation index. Step 4: Adjust the image quality evaluation index based on the aspect ratio and shape complexity of the images in the first image set to obtain the image quality evaluation index correction value. Sort the image quality evaluation index correction values ​​in order of priority, and select the image quality evaluation index correction value with the largest value as the user's optimal choice.

2. The method for generating an advertising logo pattern according to claim 1, characterized in that, The method used to obtain the feature parameters of the images in the first image set is as follows: Edge detection and contour analysis, techniques from computer-aided feature extraction, are used to perform pattern detection on the images in the first image set. This identifies the outer contours of the images and obtains their length and width. The ratio of the image's length to its width is the aspect ratio. Obtain the perimeter and area of ​​the image; the ratio of the perimeter to the area is the shape complexity. ; Based on the dominant color extraction technique in computer-integrated feature extraction technology, a clustering algorithm is used to extract the dominant color tone of each image. Then, color space conversion technology is used to obtain the brightness and color components of the image colors. Color harmony and color focus are calculated using the following formulas: in, , These represent the color harmony and color focus of the image, respectively. The index of the dominant color tone in the image, and , This represents the total number of dominant colors extracted from the image. , , It is the first The three components of the Lab color space with a primary hue. , , It is the first The three components of the Lab color space for each other color. For the index of other colors, and , For the first The proportion of each dominant color in the image, The maximum color harmony between the main color and other colors. For the first The color harmony of the main color tone in the image; Based on the layout data of the images in the first image set, the images are divided into average segments using target detection and segmentation techniques from computer-integrated feature extraction technology. The image is divided into grids, and then the total area and negative space area are obtained to calculate the blank area ratio and the uniformity of the negative space distribution. The formula used is as follows: in, , These represent the ratio of blank area and the uniformity of negative spatial distribution, respectively. , These represent the total area of ​​the negative space and the total area of ​​the image, respectively. This represents the area of ​​each grid cell. The total number of grids, The index for the number of grid cells. , For the first The area of ​​the negative space in each grid.

3. The method for generating an advertising logo pattern according to claim 2, characterized in that, Based on the extracted feature parameters of relevant design requirements, a relevant feature index for characterizing the quality of advertising logo patterns is calculated and generated. The method used is as follows: The readability index of advertising signage is calculated based on the white space ratio, color harmony, and color focus of the signage. The formula used is as follows: in, This indicates the readability index of the advertising logo; The color standardization index of advertising logos is calculated based on the color coordination and color focus of the logo design. The formula used is as follows: in, The color standardization index represents the color accuracy of advertising logo designs. For ideal color coordination, Ideal color focus; The negative space balance index of an advertising sign is calculated based on the ratio of blank area to negative space distribution uniformity. The formula used is as follows: in, The negative space balance index represents the negative space balance of the advertising logo.

4. The method for generating an advertising logo pattern according to claim 3, characterized in that... An image quality evaluation index is generated by comprehensively considering readability index, color normalization index, and negative space balance index. The formula used is as follows: in, This represents the image quality evaluation index.

5. The method for generating an advertising logo pattern according to claim 4, characterized in that, The image quality evaluation index is adjusted based on the aspect ratio and shape complexity of the images in the first image set to obtain a corrected image quality evaluation index value. These values ​​are then sorted according to priority, with the image with the highest corrected image quality evaluation index value being selected as the user's optimal choice. The method used is as follows: The image quality evaluation index is adjusted based on users' different style requirements for the logo pattern and the aspect ratio and shape complexity of the images in the first image set. The formula used is as follows: in, This represents the correction value for the image quality evaluation index. As a correction factor, Let the shape complexity of the images in the first image set be denoted as . The aspect ratio of the images in the first image set. For the ideal aspect ratio; Based on the shape complexity of the style required by the user, the image quality evaluation index correction values ​​are prioritized and sorted to obtain the user's optimal choice. The feature parameters of the first image set corresponding to the optimal choice are then used as the design parameters for the advertising logo.

6. An advertising logo pattern generating device, characterized in that, The pattern generating apparatus is used to perform the advertising sign pattern generating method according to any one of claims 1-5, including: The design requirement analysis module is used to obtain the user's design requirement data, which includes the aspect ratio, color combination, graphic elements and application scenario category of the logo pattern. Multiple images that meet the design requirement data are selected from the image library to obtain the first image set. The feature extraction and generation module is used to obtain feature parameters of images in the first image set. The feature parameters include the aspect ratio, shape complexity, color harmony, color focus, blank area ratio, and negative space distribution uniformity of the image. The image quality evaluation index calculation module is used to calculate and generate relevant feature indices for characterizing image quality based on the extracted feature parameters. The feature indices include readability index, color normalization index, and negative space balance index. The image quality evaluation index is generated by comprehensively utilizing the readability index, color normalization index, and negative space balance index. The calibration correction and optimal pattern selection module adjusts the image quality evaluation index based on the aspect ratio and shape complexity of the images in the first image set to obtain the image quality evaluation index correction value. The image quality evaluation index correction values ​​are sorted in order of priority, and the image quality evaluation index correction value with the largest value is selected as the user's optimal choice.

7. An electronic device, characterized in that, include: A processor; The processor is also connected in communication with a memory, wherein the memory stores instructions that can be executed by the processor to cause the processor to perform the advertising logo pattern generation method according to any one of claims 1-5.

8. A non-transitory computer-readable storage medium storing computer instructions, characterized in that, The computer instructions are used to cause the computer to execute the advertising logo pattern generation method according to any one of claims 1-5.

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