An automatic method and system for overlapping cross data color and transparency optimization

Abstract

The application belongs to the technical field of data visualization, and provides a color and transparency automatic optimization method and system for overlapping cross data. The method comprises the following steps: receiving classification data and drawing the classification data to a screen space to obtain specific distribution information of the classification data; based on the distribution information, the number of mixed colors, the color correlation matrix corresponding to the mixed colors, the number of pixels occupied by different colors and the neighbor relationship between different colors are counted, the intra-class correlation degree, the inter-class non-correlation degree and the color distinguishability are calculated, and the scoring function of the classification data rendering scheme is determined; taking the maximum score value of the scoring function of the classification data rendering scheme as the target, based on the preset initial solution and the preset constraint and end condition, the corresponding optimized color palette, transparency and color rendering order are obtained, and then the classification data is rendered; the spatial distribution of the data and the correlation between the colors are comprehensively considered, the perception of the user to the distribution of the data of different categories is enhanced, and the efficiency of visual analysis is improved.

Description

Technical Field

[0001] This invention belongs to the field of data visualization technology, and in particular relates to a method and system for automatically optimizing the color and transparency of overlapping and intersecting data. Background Technology

[0002] The statements in this section are merely background information related to the present invention and do not necessarily constitute prior art.

[0003] Overlapping and cross-category data are common in categorical data. Due to mutual occlusion between different categories, the data distribution in the visualization results of overlapping and cross-category data is difficult to distinguish. In such cases, semi-transparent rendering is often used to improve the efficiency of understanding the data distribution, such as overlapping histograms, Venn plots, and multivariate parallel coordinate systems. These charts help users efficiently complete data distribution comparison tasks by overlaying data from different categories. Color coding is typically used for different categories, and transparency blending techniques are used to reduce occlusion between data. However, this rendering process can create multiple new colors due to color mixing, affecting people's perception of the whole and parts of the category distribution. For example, mixing red and blue produces purple, which users may easily perceive as a separate category, thus creating ambiguity in the category representation.

[0004] To address the above issues, solutions are currently being sought from the following three different perspectives:

[0005] One solution is to use a semi-transparent effect in visualization. However, this solution may impair the perception of the data distribution represented by the background color and cannot completely eliminate incorrect colors.

[0006] Solution two is a color optimization method, which is currently designed for opaque visualizations and is not suitable for semi-transparent visualizations.

[0007] Solution three involves perceiving the data distribution within histograms, but understanding such histograms is currently difficult and influenced by many different factors.

[0008] In summary, current rendering optimization methods are not suitable for overlapping and intersecting data, making it difficult to distinguish the data distribution in the visualization results of overlapping and intersecting data, which reduces the user's perception of the distribution of different types of data and the efficiency of visual analysis. Summary of the Invention

[0009] To address the problem that current rendering optimization methods are not applicable to overlapping and intersecting data, making it difficult to distinguish the data distribution in the visualization results of overlapping and intersecting data, reducing users' perception of different data distributions and the efficiency of visual analysis, this invention provides an automatic optimization method and system for color and transparency of overlapping and intersecting data. It comprehensively considers the spatial distribution of data and the correlation between colors, and by maximizing the similarity of color names between mixed colors and category colors, it improves the connection between the whole and the parts, enhances users' perception of different data distributions, and improves the efficiency of visual analysis.

[0010] To achieve the above objectives, the present invention adopts the following technical solution:

[0011] The first aspect of the present invention provides an automatic optimization method for the color and transparency of overlapping and intersecting data.

[0012] In one or more embodiments, a method for automatically optimizing the color and transparency of overlapping data is provided, including:

[0013] Receive the classified data and draw it onto the screen space to obtain the specific distribution information of the classified data;

[0014] Based on the distribution information, the number of mixed colors, the color association matrix corresponding to the mixed colors, the number of pixels occupied by different colors, and the neighbor relationships between different colors are statistically analyzed, and then the intra-class association degree, inter-class non-association degree, and color distinguishability are calculated.

[0015] The scoring function for the classification data rendering scheme is determined by weighting the intra-class correlation, inter-class non-correlation, and color discrimination.

[0016] Taking the maximum score of the scoring function of the classification data rendering scheme as the objective function, and based on the preset initial solution and preset constraints and termination conditions, the corresponding optimized color palette, transparency and color rendering order are obtained.

[0017] Based on optimized color palettes, transparency, and color rendering order, categorical data is rendered.

[0018] Intra-class correlation is defined as the average color name similarity between each overlapping region color and multiple original colors in the overlapping region; inter-class non-correlation is defined as the average color name similarity between all unrelated colors; and color discrimination is defined as the color difference contrast based on region size.

[0019] A second aspect of the present invention provides an automatic optimization system for the color and transparency of overlapping and intersecting data.

[0020] In one or more embodiments, an automatic color and transparency optimization system for overlapping cross data includes:

[0021] A classification data loading module is used to receive classification data and draw it onto the screen space to obtain the specific distribution information of the classification data.

[0022] The evaluation index calculation module is used to calculate the number of mixed colors, the color association matrix corresponding to the mixed colors, the number of pixels occupied by different colors, and the neighbor relationship between different colors based on the distribution information, and then calculate the intra-class association degree, inter-class non-association degree, and color discrimination degree.

[0023] The scoring function determination module is used to determine the scoring function of the classification data rendering scheme based on the weighted sum of intra-class correlation, inter-class non-correlation and color discrimination.

[0024] The rendering scheme optimization module is used to obtain the corresponding optimized color palette, transparency and color rendering order based on the preset initial solution and preset constraints and termination conditions, with the objective function being to maximize the score value of the scoring function of the classification data rendering scheme.

[0025] The categorical data rendering module is used to render categorical data based on an optimized color palette, transparency, and color rendering order.

[0026] Intra-class correlation is defined as the average color name similarity between each overlapping region color and multiple original colors in the overlapping region; inter-class non-correlation is defined as the average color name similarity between all unrelated colors; and color discrimination is defined as the color difference contrast based on region size.

[0027] A third aspect of the present invention provides a computer-readable storage medium.

[0028] A computer-readable storage medium having a computer program stored thereon that, when executed by a processor, implements the steps in the automatic optimization method for color and transparency of overlapping cross data as described above.

[0029] A fourth aspect of the present invention provides an electronic device.

[0030] An electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the program, implements the steps in the automatic optimization method for color and transparency of overlapping cross data as described above.

[0031] Compared with the prior art, the beneficial effects of the present invention are:

[0032] (1) This invention utilizes the distribution information of classified data to statistically analyze the number of mixed colors, the color association matrix corresponding to the mixed colors, the number of pixels occupied by different colors, and the neighbor relationships between different colors. It calculates the intra-class correlation, inter-class non-correlation, and color distinguishability, and then uses weighted summation to obtain the scoring function of the classified data rendering scheme. Then, taking the maximum score value of the scoring function of the classified data rendering scheme as the objective function, based on the preset initial solution and preset constraints and termination conditions, it obtains the corresponding optimized color palette, transparency, and color rendering order, and then renders the classified data. It comprehensively considers the spatial distribution of data and the correlation between colors. By maximizing the color name similarity between mixed colors and category colors, it improves the connection between the whole and the parts, enhances the user's perception of the distribution of different categories of data, and improves the efficiency of visual analysis.

[0033] (2) Based on the distribution of classified data, this invention uses simulated annealing algorithm to automatically generate color scheme. Considering the data distribution, and combining the similarity of color names and the color differentiation between colors, the invention improves the effect of semi-transparent data visualization by optimizing the color, transparency and rendering order assigned to each data category, thereby improving the efficiency of visual analysis, especially data comparison tasks. Attached Figure Description

[0034] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an improper limitation of the invention.

[0035] Figure 1 This is a schematic diagram illustrating the principle of the automatic optimization method for color and transparency of overlapping and intersecting data according to an embodiment of the present invention.

[0036] Figure 2 This is a flowchart of the automatic optimization method for color and transparency of overlapping and intersecting data according to an embodiment of the present invention;

[0037] Figure 3(a) is a schematic diagram of pre-calculating the pixel area occupied by different color blocks on the overlapping histogram according to an embodiment of the present invention;

[0038] Figure 3(b) is a schematic diagram of pre-calculating different color blocks and member relationships on an overlapping histogram according to an embodiment of the present invention;

[0039] Figure 3(c) is a schematic diagram of pre-calculating the membership matrix on the overlapping histogram according to an embodiment of the present invention;

[0040] Figure 3(d) is a schematic diagram of pre-calculating the adjacency relationship diagram of different colors on the overlapping histogram according to an embodiment of the present invention;

[0041] Figure 4(a) is a schematic diagram of the optimization results of the weight parameter setting (1, 0, 0) in the overlapping histogram according to an embodiment of the present invention.

[0042] Figure 4(b) is a schematic diagram of the optimization results of the weight parameter settings (0, 1, 0) in the overlapping histogram according to an embodiment of the present invention.

[0043] Figure 4(c) is a schematic diagram of the optimization results of the weight parameter settings (0, 0, 1) in the overlapping histogram according to an embodiment of the present invention.

[0044] Figure 4(d) is a schematic diagram of the optimization results of the weight parameter setting (1, 1, 1) in the overlapping histogram according to an embodiment of the present invention.

[0045] Figure 5 This is a schematic diagram illustrating the results of applying the present invention to a parallel coordinate system according to an embodiment of the invention;

[0046] Figure 6 This is a schematic diagram illustrating the results of applying the present invention to a Venn diagram according to an embodiment of the invention;

[0047] Figure 7 This is a schematic diagram of the automatic optimization system for color and transparency of overlapping and intersecting data according to an embodiment of the present invention.

[0048] Figure 8 This is a schematic diagram of an electronic device according to an embodiment of the present invention. Detailed Implementation

[0049] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0050] It should be noted that the following detailed description is illustrative and intended to provide further explanation of the invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0051] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the scope of exemplary embodiments according to the invention. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0052] Figure 1 This is a schematic diagram illustrating the principle of the automatic optimization method for color and transparency of overlapping and intersecting data according to an embodiment of the present invention. Figure 2 This is a flowchart illustrating the automatic optimization method for color and transparency of overlapping and intersecting data according to an embodiment of the present invention. (Combined with...) Figure 1 and Figure 2 The automatic optimization method for the color and transparency of overlapping data in this embodiment may include:

[0053] S101, Receive the classification data and draw it onto the screen space to obtain the specific distribution information of the classification data.

[0054] The classification data here includes overlapping histograms, parallel coordinate systems, and Venn plots, as shown in Figure 4. Figure 5 and Figure 6 As shown.

[0055] Given a classification dataset containing m data distributions M = {1, ..., m}, and a canvas background color c... b The goal is to find an appropriate color c for each data distribution. i The corresponding transparency attribute α i And the rendering order of the color. i The final coloring scheme includes a color set P = {c1, ..., c}. m The corresponding transparency set A = {α1, ..., α} m}, and the color rendering order O=<o1,...,o m >

[0056] S102, based on the distribution information, statistically analyze the number of mixed colors, the color association matrix corresponding to the mixed colors, the number of pixels occupied by different colors, and the neighbor relationships between different colors, such as... Figures 3(a)-3(d) Then, the intra-class association degree, inter-class non-association degree, and color discrimination degree are calculated.

[0057] Membership matrix M, containing b×m elements, shows the relationships between each mixed color and different categories, where n represents the number of mixed colors and m represents the number of data categories. ij R represents the i-th mixed color region i It is part of the distribution of the j-th class (n≥i>m, m≥j≥1);

[0058] Adjacency diagram of different colors, which shows the spatial distribution relationship between different mixed colors;

[0059] The pixel area occupied by different color blocks is used to calculate the overall canvas size occupied by different color regions. i Indicates the mixed color region R i Size.

[0060] Intra-class correlation is defined as the average color name similarity between each overlapping region color and multiple original colors in the overlapping region; inter-class non-correlation is defined as the average color name similarity between all unrelated colors; and color discrimination is defined as the color difference contrast based on region size.

[0061] In this embodiment, for each overlapping region, its correlation is defined as the color name similarity between the color of that region and multiple original colors of the overlapping region.

[0062] Based on this definition, intra-class association is defined as the average color name similarity between each overlapping region color and multiple original colors of the overlapping region.

[0063] Intraclass association level E WA The expression for (P, A, O) is:

[0064]

[0065] Where Ψ(i) represents the weight term; M j，i R represents the j-th mixed color region. j It is part of the i-th class distribution; n represents the number of mixed colors, m represents the number of data categories; M i ·M j This indicates whether the i-th region and the j-th region belong to the same class. When i and j are related, M... i ·M j It is 1 if it is not 1, otherwise it is 0.

[0066] S(c i c j This represents a color name similarity measurement method. This method is based on a color-name count matrix T compiled from data collected from large-scale online user experiments. Heer and Stone used cosine distance to measure the similarity between two colors c. i and c j Color name similarity between:

[0067]

[0068] in, Indicates color c i The larger the probability distribution of color names, the greater the S(c) i c j The more similar the names of two colors, the better. Furthermore, a weight term Ψ(i) based on class data distribution is integrated into the intra-class association degree. This term comprises two components: first, classes with more mixed color patches should be prioritized for optimization to achieve better color association; second, if a class has a large proportion of overlapping and non-overlapping parts, its corresponding weight should also be increased. Therefore, Ψ(i) is ultimately defined as:

[0069]

[0070] in, This represents the number of overlapping regions in the i-th class. Let Ψ(i) represent the total area of ​​the i-th class. In short, the value of Ψ(i) allows the user to consciously assign a degree of difficulty to the perceived data distribution i, so that difficult-to-identify overlapping regions are prioritized during the optimization process.

[0071] For mixed colors that do not belong to the same category, the distinction between categories is improved by reducing the similarity of their color names. Therefore, the degree of disassociation between categories, E... BD (P, A, O) is defined as the average color name similarity among all unrelated colors:

[0072]

[0073] The first part of the score represents the average color name similarity among all unrelated colors, while the second part, the max function, is used to avoid large differences between different colors.

[0074] The scoring function aims to maximize the color difference between different colors while ensuring higher contrast with surrounding areas for smaller regions. To achieve this, we define color discrimination using a nearest neighbor graph that includes the region size. Considering that region size affects the perception of color difference (DASzafir. Modeling colordifference for visualization design. IEEE Transactions on Visualization and Computer Graphics, 24(1): 392-401, 2018. doi: 10.1109 / TVCG.2017.2744359), we define color discrimination as:

[0075]

[0076] Wherein, Ω(R) i S represents all neighbors of the i-th region. i Representing region R i The size, here we will S i Normalization is performed based on the size of all regions, therefore S i ∈(0,1), larger S i This indicates that the region has a larger area. To further ensure sufficient distinguishability between different regions, we introduce two hard constraints:

[0077] The minimum distinguishable difference constraint states that any two colors must be distinguishable.

[0078]

[0079] Wherein, D(c i cj ) represents two colors c i and c j The CIEDE2000 color distance between (G. Sharma, W. Wu. and E. N. Dalal. The CIEDE2000 color-difference formula: implementation notes, supplementary test data, and mathematical observations. Color Research & Application, 30(1): 21-30, 2005. doi: 10.1002 / co1.20070), where η represents the minimum perceptible difference threshold, which is set to 3 by default (Y. Yang, J. Ming, and N. Yu. Color image quality assessment based on CIEDE2000. Advances in Multimedia, 2012: 11-11, 2012. doi: 10.1155 / 2012 / 273723).

[0080] Background contrast constraint, meaning that the color of each area should have sufficient contrast with the background color for better perception:

[0081]

[0082] Among them, LD(c i c b ) represents color c i With background color c b The absolute value of the brightness difference between the two values; σ is a constant coefficient, set to 5 by default.

[0083] S103. Based on the weighted sum of intra-class correlation, inter-class non-correlation, and color discrimination, determine the scoring function for the classification data rendering scheme.

[0084] The specific scoring function is as follows:

[0085]

[0086] Wherein, each weight term ω i It is a value between [0, 1], with a default setting of 1. The three optimization options are Within-class Association (WA), Between-class Disassociation (BD), and Color Separability (CS).

[0087] This invention sets user-adjustable weighting coefficients when calculating the overall visualization score. By adjusting these coefficients, visualization results with different emphases can be generated, such as better discrimination or better class association.

[0088] S104: Taking the maximum score of the scoring function of the classification data rendering scheme as the objective function, and based on the preset initial solution and preset constraints and termination conditions, the corresponding optimized color palette, transparency and color rendering order are obtained.

[0089] Optimize the color palette, transparency, and color rendering order under minimum discernible difference constraints and background contrast constraints.

[0090] If no color palette is provided, multiple colors are randomly selected from the discretized color space as the initial color palette. In the subsequent optimization process, the three attribute values ​​of brightness, hue and saturation of each color need to be perturbed to generate a new color palette.

[0091] Discretized color spaces, such as the CIE Lab color space, are uniform color spaces defined by the CIE for color measurement. They are also the most widely used color spaces in colorimeters. This color space uses three dimensions to quantitatively describe color.

[0092] CIE Lab color space is short for CIE1976 L*a*b* uniform color space. The CIE1976 L*a*b* uniform color space is a non-linear transformation of the CIE1931 standard colorimetric system. It converts the XYZ rectangular coordinate color space into cylindrical polar coordinates, transforming the tristimulus values ​​XYZ into lightness L* and chromaticity a* and b* consistent with human vision. Here, a* and b* correspond to the perception of hue and saturation.

[0093] If a color palette is given, it will be used as the initial color palette. In subsequent optimization processes, a new color palette will be generated by recombinizing the colors of the current color palette.

[0094] The objective function is solved based on the simulated annealing algorithm, and the process is as follows:

[0095] S1041: Randomly select m colors from the discretized color space to form the initial solution (m is the number of classes in the data), then initialize the transparency of each color, and then initialize the rendering order of the m colors. Set the initial temperature, cooling coefficient, and minimum temperature.

[0096] S1042, check if the current temperature is greater than the minimum temperature. If yes, proceed to the next step; otherwise, exit the iteration and return the final result.

[0097] S1043, randomly perturb the initial solution to obtain a new solution. Considering that the final shading scheme consists of three parts, namely the color, transparency, and rendering order of each class, in each iteration, the color, transparency, or rendering order is randomly perturbed with the same probability. If a palette color is given, the positions of two random colors in the palette are randomly changed; otherwise, the color value of a certain color is changed.

[0098] S1044, check whether the new solutions all satisfy the minimum distinguishable difference constraint and the background contrast constraint. If not, perturb the colors until they are satisfied.

[0099] S1045, score the current solution;

[0100] S1046, if the score of the current solution is better than the previous solution, then accept the current solution; otherwise, use probability exp(ΔE / T) to process the solution. t Accept the current solution, where ΔE represents the difference in score between the current solution and the previous solution.

[0101] T t This is the current temperature.

[0102] S1047, reduce the temperature according to the cooling coefficient, and return to step S1042 to continue the iteration.

[0103] S105 renders categorical data based on an optimized color palette, transparency, and color rendering order.

[0104] This invention employs a simulated annealing algorithm to automatically solve for the optimal coloring scheme. The scoring of the solution combines color name similarity, color distinguishability, spatial distance, minimum perceptible difference, and background contrast. The final coloring result optimizes the color, transparency, and rendering order of each data category, which can effectively reduce the generation of incorrect colors, enhance the overall perception of different colors in the same category, and improve the efficiency of data visual analysis.

[0105] It should be noted that in other embodiments, in addition to using the simulated annealing algorithm to solve the objective function, other existing algorithms, such as particle swarm optimization, can also be used to solve it, which will not be described in detail here.

[0106] Figure 7 This is a schematic diagram of an automatic optimization system for color and transparency of overlapping and cross-data according to an embodiment of the present invention. This embodiment is related to... Figure 1 Corresponding to the automatic optimization method for the color and transparency of overlapping and cross-cutting data, such as... Figure 7 As shown, the automatic optimization system for the color and transparency of overlapping data in this embodiment may include:

[0107] The classification data loading module 201 is used to receive classification data and draw it onto the screen space to obtain the specific distribution information of the classification data.

[0108] The evaluation index calculation module 202 is used to calculate the number of mixed colors, the color association matrix corresponding to the mixed colors, the number of pixels occupied by different colors, and the neighbor relationship between different colors based on the distribution information, and then calculate the intra-class association degree, inter-class non-association degree, and color discrimination degree.

[0109] The scoring function determination module 203 is used to determine the scoring function of the classification data rendering scheme based on the weighted sum of intra-class correlation, inter-class non-correlation and color discrimination.

[0110] The rendering scheme optimization module 204 is used to obtain the corresponding optimized color palette, transparency and color rendering order based on the preset initial solution and preset constraints and termination conditions, with the objective function being to maximize the score value of the scoring function of the classification data rendering scheme.

[0111] The classification data rendering module 205 is used to render classification data based on an optimized color palette, transparency, and color rendering order.

[0112] Intra-class correlation is defined as the average color name similarity between each overlapping region color and multiple original colors in the overlapping region; inter-class non-correlation is defined as the average color name similarity between all unrelated colors; and color discrimination is defined as the color difference contrast based on region size.

[0113] Specifically, in the rendering scheme optimization module 204, the color palette, transparency, and color rendering order are optimized under the constraints of minimum distinguishable difference and background contrast.

[0114] In the rendering scheme optimization module 204, if no color palette is provided, multiple colors are randomly selected from the discretized color space as the initial color palette. In the subsequent optimization process, the three attribute values ​​of brightness, hue and saturation of each color need to be perturbed to generate a new color palette.

[0115] Discretized color spaces, such as the CIE Lab color space, are uniform color spaces defined by the CIE for color measurement. They are also the most widely used color spaces in colorimeters. This color space uses three dimensions to quantitatively describe color.

[0116] CIE Lab color space is short for CIE1976 L*a*b* uniform color space. The CIE1976 L*a*b* uniform color space is a non-linear transformation of the CIE1931 standard colorimetric system. It converts the XYZ rectangular coordinate color space into cylindrical polar coordinates, transforming the tristimulus values ​​XYZ into lightness L* and chromaticity a* and b* consistent with human vision. Here, a* and b* correspond to the perception of hue and saturation.

[0117] In the rendering scheme optimization module 204, if a color palette is given, the given color palette is used as the initial color palette, and a new color palette is generated by recombinizing the colors of the current color palette in the subsequent optimization process.

[0118] Figure 7 The specific implementation process of modules 201-205 in the automatic optimization system for the color and transparency of the overlapping cross data shown is as follows: Figure 1 The specific implementation process of steps S101 to S105 is the same, and will not be described in detail here.

[0119] Reference Figure 8 A schematic diagram of an electronic device is provided. It should be noted that... Figure 8 The electronic device 100 shown is merely an example and should not be construed as limiting the functionality and scope of use of the embodiments of the present invention.

[0120] like Figure 8 As shown, the electronic device 300 includes a central processing unit (CPU) 301, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 302 or a program loaded from a storage section 108 into a random access memory (RAM) 303. The RAM 303 also stores various programs and data required for system operation. The CPU 301, ROM 302, and RAM 303 are interconnected via a bus 304. An input / output (I / O) interface 305 is also connected to the bus 304.

[0121] The following components are connected to I / O interface 305: an input section 306 including a keyboard, mouse, etc.; an output section 307 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and speakers, etc.; a storage section 308 including a hard disk, etc.; and a communication section 309 including a network interface card such as a local area network (LAN) card, modem, etc. The communication section 309 performs communication processing via a network such as the Internet. Drive 110 is also connected to I / O interface 305 as needed. Removable media 311, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., are installed on drive 310 as needed so that computer programs read from them can be installed into storage section 308 as needed.

[0122] When the central processing unit 301 in the electronic device of this embodiment executes the program, it achieves the following: Figure 1 The steps in the automatic optimization method for color and transparency of overlapping cross data are shown.

[0123] Specifically, according to embodiments of this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program including functions for executing... Figure 1 The program code for the method shown. In such an embodiment, the computer program can be downloaded and installed from a network via communication section 309, and / or installed from removable medium 311. When the computer program is executed by central processing unit 301, it performs the various functions defined in the apparatus of this application.

[0124] in, Figure 1 The computer program instructions corresponding to the method shown may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to operate in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in the process. Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0125] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. The storage medium can be a magnetic disk, optical disk, read-only memory (ROM), or random access memory (RAM), etc.

[0126] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for automatically optimizing the color and transparency of overlapping and intersecting data, characterized in that, include: Receive the classified data and draw it onto the screen space to obtain the specific distribution information of the classified data; Based on the distribution information, the number of mixed colors, the color association matrix corresponding to the mixed colors, the number of pixels occupied by different colors, and the neighbor relationships between different colors are statistically analyzed, and then the intra-class association degree, inter-class non-association degree, and color distinguishability are calculated. The scoring function for the classification data rendering scheme is determined by weighting the intra-class correlation, inter-class non-correlation, and color discrimination. Taking the maximum score of the scoring function of the classification data rendering scheme as the objective function, and based on the preset initial solution and preset constraints and termination conditions, the corresponding optimized color palette, transparency and color rendering order are obtained. Based on optimized color palettes, transparency, and color rendering order, categorical data is rendered. Intra-class association is defined as the average color name similarity between each overlapping region color and multiple original colors of the overlapping region; intra-class association degree The expression is: in, A represents the color set; A represents the transparency set; O represents the color rendering order. Indicates the weighted terms; Indicates the first Mixed color areas It is the first A portion of the class distribution; Indicates the amount of mixed colors. Indicates the number of data categories; Indicates the first The region and the first Whether the regions belong to the same category, when and When there is a relationship, =1, otherwise =0; Representing two colors and Similarity of color names between them; Inter-class disassociation is defined as the average color name similarity among all unrelated colors; the degree of inter-class disassociation. The expression is: The first part of the score represents the average color name similarity among all unrelated colors, while the second part, the max function, is used to avoid large differences between different colors. Color discrimination is defined as the color difference contrast based on region size; color discrimination The expression is: in, Indicates the first All neighbors in that area, Indicates the area Size, Representing two colors and The CIEDE2000 color distance between them.

2. The automatic optimization method for color and transparency of overlapping and intersecting data as described in claim 1, characterized in that, Optimize the color palette, transparency, and color rendering order under minimum discernible difference constraints and background contrast constraints.

3. The automatic optimization method for color and transparency of overlapping and intersecting data as described in claim 1, characterized in that, If no color palette is given, multiple colors are randomly selected from the discretized color space as the initial color palette. In the subsequent optimization process, the three attribute values ​​of lightness, hue and saturation of each color need to be perturbed to generate a new color palette. If a color palette is given, it will be used as the initial color palette. In subsequent optimization processes, a new color palette will be generated by recombinizing the colors of the current color palette.

4. The automatic optimization method for color and transparency of overlapping and intersecting data as described in claim 1, characterized in that, If a color palette is given, the colors in the current palette are recombined.

5. The automatic optimization method for color and transparency of overlapping and intersecting data as described in claim 1, characterized in that, The objective function is solved based on the simulated annealing algorithm.

6. An automatic optimization system for the color and transparency of overlapping and intersecting data, characterized in that, include: A classification data loading module is used to receive classification data and draw it onto the screen space to obtain the specific distribution information of the classification data. The evaluation index calculation module is used to calculate the number of mixed colors, the color association matrix corresponding to the mixed colors, the number of pixels occupied by different colors, and the neighbor relationship between different colors based on the distribution information, and then calculate the intra-class association degree, inter-class non-association degree, and color discrimination degree. The scoring function determination module is used to determine the scoring function of the classification data rendering scheme based on the weighted sum of intra-class correlation, inter-class non-correlation and color discrimination. The rendering scheme optimization module is used to obtain the corresponding optimized color palette, transparency and color rendering order based on the preset initial solution and preset constraints and termination conditions, with the objective function being to maximize the score value of the scoring function of the classification data rendering scheme. The categorical data rendering module is used to render categorical data based on an optimized color palette, transparency, and color rendering order. Intra-class association is defined as the average color name similarity between each overlapping region color and multiple original colors of the overlapping region; intra-class association degree The expression is: in, A represents the color set; A represents the transparency set; O represents the color rendering order. Indicates the weighted terms; Indicates the first Mixed color areas It is the first A portion of the class distribution; Indicates the amount of mixed colors. Indicates the number of data categories; Indicates the first The region and the first Whether the regions belong to the same category, when and When there is a relationship, =1, otherwise =0; Representing two colors and Similarity of color names between them; Inter-class disassociation is defined as the average color name similarity among all unrelated colors; the degree of inter-class disassociation. The expression is: The first part of the score represents the average color name similarity among all unrelated colors, while the second part, the max function, is used to avoid large differences between different colors. Color discrimination is defined as the color difference contrast based on region size; color discrimination The expression is: in, Indicates the first All neighbors in that area, Indicates the area Size, Representing two colors and The CIEDE2000 color distance between them.

7. The automatic optimization system for color and transparency of overlapping and intersecting data as described in claim 6, characterized in that, In the rendering scheme optimization module, the color palette, transparency, and color rendering order are optimized under the constraints of minimum discernible difference and background contrast.

8. The automatic optimization system for color and transparency of overlapping and intersecting data as described in claim 6, characterized in that, In the rendering scheme optimization module, if no color palette is provided, multiple colors are randomly selected from the discretized color space as the initial color palette. In the subsequent optimization process, the three attribute values ​​of brightness, hue and saturation of each color need to be perturbed to generate a new color palette. If a color palette is given, it will be used as the initial color palette. In subsequent optimization processes, a new color palette will be generated by recombinizing the colors of the current color palette.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the steps in the automatic optimization method for color and transparency of overlapping cross data as described in any one of claims 1-5.

10. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps in the automatic optimization method for color and transparency of overlapping cross data as described in any one of claims 1-5.

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