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A method and system for generating palette based on data distribution

A data distribution and color palette technology, applied in the field of data visualization, can solve the problems of taking into account the contrast, limited geographic data, and the incompatibility of the optimization results, so as to improve the efficiency and improve the effect.

Active Publication Date: 2021-03-30
SHANDONG UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

But he has two flaws: a) he ignores aesthetics; b) he is limited by geographic data
However, two flaws of this method limit its applicability: First, it does not take the contrast between the color and the background into account, resulting in optimization results that are not suitable for all scenarios
Second, it is designed for the visualization of map data and cannot support more classification information visualization tasks
Rather than optimizing a color palette for a specific visualization, Fang et al. (H. Fang, S. Walton, E. Delahaye, J. Harris, D. Storchak, and M. Chen. Categorical colormap optimization with visualization case studies. IEEE Trans. Vis .&Comp.Graphics,23(1):871–880,2017.doi: 10.1109 / tvcg.2016.2599214) propose a method to maximize the perceived distance between given colors, although this method can combine different user-specified constraints, It does not consider the data distribution, and therefore the generated visualization cannot well show the data structure between different classes

Method used

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  • A method and system for generating palette based on data distribution
  • A method and system for generating palette based on data distribution
  • A method and system for generating palette based on data distribution

Examples

Experimental program
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Embodiment 1

[0045] This embodiment provides a palette generation method based on data distribution, such as figure 1 As shown, it specifically includes the following steps:

[0046] Step 1: Load classification data and color data, the color data contains discretized LAB color space.

[0047] The classified data are scatter plots, line charts, histograms and the like.

[0048] When loading the color data, some colors that most users don’t like will be removed, such as colors with brightness less than 35 or brightness greater than 95, colors with brightness between 35 and 75 and hue between 85 and 114, most of these colors are yellow-green.

[0049] Step 2: Project the classified data into the visible space (screen space), and obtain the location data of the classified data.

[0050] When the classification data is a scatter diagram, obtaining the position of each point in various types of scatter;

[0051] When the classification data is a histogram, obtain the position of the geometri...

Embodiment 2

[0113] The purpose of this embodiment is to provide a palette generation system based on data distribution.

[0114] In order to achieve the above purpose, this embodiment provides a palette generation system based on data distribution, including:

[0115] The data loading module receives classification data and color data, and the color data includes a discretized color space;

[0116] A data distribution determination module, projecting the classified data into a visible space, and obtaining the location information of the classified data;

[0117] A degree of separation measurement module, based on the position information to measure the degree of separation between classes;

[0118] The palette optimization module randomly selects multiple colors from the discretized color space as the initial solution, combines the separation between classes, finds an approximate optimal solution based on the simulated annealing algorithm, and generates a palette;

[0119] The data rend...

Embodiment 3

[0121] The purpose of this embodiment is to provide an electronic device.

[0122] In order to achieve the above object, this embodiment provides an electronic device, including a memory, a processor, and a computer program stored on the memory and operable on the processor. When the processor executes the program, the following steps are implemented, including :

[0123] receiving categorical data and color data, the color data comprising a discretized color space;

[0124] projecting the classified data into a visual space, and obtaining position information of the classified data;

[0125] Measuring the degree of separation between classes based on the location information;

[0126]Randomly select multiple colors from the discretized color space as the initial solution, combine the separation between classes, find an approximate optimal solution based on the simulated annealing algorithm, and generate a color palette;

[0127] Renders categorical data based on the palett...

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PUM

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Abstract

A palette generation method and system based on data distribution. The method comprises the following steps: receiving classification data and color data, the color data comprising a discretized color space; projecting the classification data into a visual space to obtain position information of the classification data; randomly selecting a plurality of colors from the discretized color space as an initial solution, and searching for an approximate optimal solution on the basis of a simulated annealing algorithm, to generate a palette; and rendering the classification data on the basis of the palette. The palette generation method takes into account factors such as data distribution, color differentiation, and aesthetics, and therefore has a good visual effect and improves the visual analysis efficiency.

Description

technical field [0001] The technical field of data visualization of the present invention particularly relates to a method and system for generating palettes based on data distribution. Background technique [0002] Common methods for visualizing categorical data include histograms, line charts, and scatter plots. Usually, each class is represented by a color. The main task is to better distinguish between different classes. The degree to which people perceive different classes is greatly influenced by color, yet how to find an appropriate color palette is still a complex and time-consuming task, even for experts. [0003] There are currently three main methods for palette design for classified data, namely, palette generation, palette color assignment, and palette color optimization: [0004] (1) Palette generation [0005] Generating suitable color palettes for categorical data has received much attention in the field of visual design, and most existing color selection m...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F16/904
CPCG06F16/904
Inventor 汪云海卢克成
Owner SHANDONG UNIV