Method for obtaining a color sub-palette from an emotion board

Abstract

The invention provides a method of obtaining a color sub-palette from a first mood board comprising one or more digital color images, the method comprising the steps of: (1) generating a first color palette having k1 representative colors from the first mood board by a method that takes into account under-represented colors, (2) providing a second color palette having k2 colors, (3) ranking the colors in the first color palette according to a scoring algorithm that assigns a score to each color in the first color palette based on the color difference of each color in the first color palette to each color in the second color palette, and (4) selecting a subset of k sub colors from the first color palette, the subset having scores corresponding to the smallest color differences to the colors in the second color palette, the subset of colors being a color sub-palette. The invention also provides a data processing device and a computer program product.

Description

Technical Field

[0001] This invention relates to a method for obtaining a color subpalette from a mood board. The color subpalette is obtained from a color palette containing representative colors (mood boards) of a set of digital color images. Background Technology

[0002] Consumers wishing to choose one or more paint colors, for example, for decorating a room, are presented with a vast array of color options. Furthermore, selecting multiple paint colors that coordinate with each other, or, for example, with the color of a piece of furniture, is a highly subjective and potentially daunting task. Paint manufacturers typically provide pre-arranged or curated color palettes that may not reflect consumer preferences. Therefore, this invention aims to facilitate the selection process by providing consumers with personalized color palettes based on one or more color images (mood boards) selected by the consumer.

[0003] Determining a set of representative colors from a digital color image is useful for many applications. For example, to facilitate paint color selection, the method described herein can provide a color palette with representative colors based on one or more digital color images provided by a user. A user can, for example, collect and input a set of one or more digital color images they like, and a color palette is provided to the user based on the colors in the input images. Several color images can be input individually or constitute a single mood board image or collage. Each image in the set of images can be weighted differently to express certain preferences within the set. For example, some images constituting the mood board can be magnified to amplify the weight of colors in that particular image. US2021 / 075329A1 describes a method that transforms a digital image by calculating an image color palette based on the image; maps the colors of the image color palette to the colors of a conceptual color palette representing a mood or scene; and transforms the colors of the digital image based on this mapping.

[0004] WO 2021 / 209413A1 describes a method for determining a color palette from a digital image. The method includes defining a predetermined number of K cluster centers in a color space; forming clusters by associating pixels with the nearest cluster center; reducing the number of clusters to k by deleting and / or merging cluster centers according to a predefined threshold; and defining a corresponding representative color for each of the resulting k clusters.

[0005] When generating a color palette from a mood board, many representative colors can be identified, especially if the mood board contains many visually distinct images. Typically, to provide a good representation of all the important colors found in the images on the mood board, the palette needs to contain more than eight colors, usually more than twelve, and sometimes as many as sixteen or more. However, such a large number of colors in a palette is inconvenient for users who need to choose paint colors for decoration. In interior design schemes, typically only a few colors are used, such as two or three, and usually no more than five. This means that users need to select only a few colors from a large color palette created from their mood board, which can be difficult for users without experience in color design.

[0006] Therefore, it is desirable to provide a method for obtaining a color palette from a mood board with only a limited number of colors, such as five or fewer. The colors should be representative of the imagery contained in the mood board and obtained through a meaningful reduction in the number of colors in the color palette created from the user's mood board. It is also desirable that this method reflects the user's preferences regarding other important aspects of interior design, such as preferred design styles or currently trendy colors. Summary of the Invention

[0007] This invention provides a method for obtaining a color sub-palette from a first mood board comprising one or more digital color images, the method comprising the following steps:

[0008] (1) Generate a first color palette with k1 representative colors based on the first mood palette.

[0009] (2) Provide a second color palette with k2 colors.

[0010] (3) Sort the colors in the first color palette according to a scoring algorithm, wherein the scoring algorithm assigns a score to each color in the first color palette based on the color difference between each color in the first color palette and each color in the second color palette, and

[0011] (4) Select k from the first color palette sub A subset of colors, the subset having a fraction corresponding to the minimum color difference with a color in the second color palette, the subset of colors being the color sub-palette.

[0012] The first color palette is obtained from the first mood board through a method including the following steps:

[0013] (a) Obtain at least one digital color image having p pixels, each pixel having a color value in an n-dimensional color space;

[0014] (b) Ignoring the obtained image, define a predetermined number of K cluster centers, which are distributed along multiple lines in the cubic RGB color space, and the multiple lines intersect at the intersection point located at the center of the cubic RGB color space, where K > k1;

[0015] (c) forming clusters by associating the color value of each pixel with the nearest cluster center; and after forming the clusters...

[0016] c1) For each cluster, redetermine the cluster centers;

[0017] (d) Reduce the number of clusters to k1 by deleting cluster centers and / or merging clusters, where deletion includes:

[0018] - For each cluster, determine the number of pixels associated with that cluster, and

[0019] - If the number of pixels associated with a cluster center is lower than a predefined pruning threshold, delete the cluster center;

[0020] And the merger includes:

[0021] - Determine the distance between two clusters, and

[0022] -Merge clusters whose distance is less than a predefined merging threshold;

[0023] Among them, iterative steps c), c1), and d); and

[0024] (e) Define a representative color for each of the resulting k1 clusters.

[0025] (f) Based on k1 representative colors, form the first color palette.

[0026] In a second aspect, the present invention provides a method for selecting paint colors, the method comprising the method described above, and further comprising selecting a paint color corresponding to each color of the color sub-palette.

[0027] In a third aspect, the present invention provides a data processing apparatus comprising means for performing the steps of the method of the present invention.

[0028] In a fourth aspect, the present invention provides a computer program product including instructions that, when executed by a computer, cause the computer to perform the steps of the method of the present invention. Attached Figure Description

[0029] Figure 1-3 Each method is illustrated with a schematic flowchart.

[0030] Figure 4 A schematic example is shown of a predetermined number of K predefined cluster centers distributed in a color space according to a predetermined pattern. Detailed Implementation

[0031] This invention is based on the insightful observation that the number of colors in a large color palette obtained from a mood board can be reduced, and a subset of the color palette (color sub-palette) can be created in a way that is meaningful to the user. Meaningful to the user means that the color selection is not random and takes into account other user preferences, such as specific themes, preferred design styles, or current trending colors.

[0032] This method results in a color sub-palette containing fewer colors than the original color palette obtained from the mood palette, and only colors that best match the user's preferences represented by another (second) color palette.

[0033] In step (1) of the method according to the invention, a first color palette having k1 representative colors is generated based on the digital color images contained in the first mood palette. The mood palette is a collection of digital color images containing at least one image, and preferably at least two images. The mood palette can be created by the user by selecting images that are appealing to the user.

[0034] A color palette is a set of at least two colors. A color palette subset (color sub-palette) is a color palette with a reduced number of colors compared to the original color palette, wherein the color palette subset is obtained from the original color palette. The colors in a color palette subset represent a subset of the colors derived from the original color palette.

[0035] The first color palette comprises color representations from one or more digital color images in the first mood palette. The number of colors k1 in the first color palette is preferably at least 8, or at least 12, or more preferably at least 16.

[0036] A color palette is generated from a first mood board using a method that considers underrepresented (emphasized) colors contained within the mood board. Underrepresented or emphasized colors are those that occupy only a small area of ​​a digital image relative to other colors but contrast strongly with their surroundings. Such colors are prominent to human observers but are easily overlooked by conventional color palette generation algorithms that only include the colors that occupy the largest area of ​​the digital image in the color palette. For example, a mood board may consist of several internally designed images, where dark shades of blue and gray are most prevalent. However, some of the images constituting the mood board may contain small, bright yellow features, such as a yellow pillow, which stand out against a predominantly dark blue and gray environment. Overall, this bright yellow only covers a small portion of the mood board—that is, only a few pixels are yellow—but its presence is clearly perceptible to the user and may be present in the mood board for this reason.

[0037] This method is known, for example, from WO 2021 / 209413 A1, the contents of which are incorporated herein by reference.

[0038] Specifically, the first color palette is obtained by a method including the following steps:

[0039] (a) Obtain at least one digital color image having p pixels, each pixel having a color value in an n-dimensional color space;

[0040] (b) Ignoring the obtained image, define a predetermined number of K cluster centers, which are distributed along multiple lines in the cubic RGB color space, and these multiple lines intersect at the intersection point located at the center of the cubic RGB color space, where K > k1;

[0041] (c) Clusters are formed by associating the color value of each pixel with the nearest cluster center; and after clustering is formed...

[0042] c1) For each cluster, redetermine the cluster centers;

[0043] (d) Reduce the number of clusters to k1 by deleting cluster centers and / or merging clusters, where deletion includes:

[0044] - For each cluster, determine the number of pixels associated with that cluster, and

[0045] - If the number of pixels associated with a cluster center is less than a predefined pruning threshold, delete the cluster center; and wherein merging includes:

[0046] - Determine the distance between two clusters, and

[0047] -Merge clusters whose distance is less than a predefined merging threshold;

[0048] Among them, iterative steps c), c1), and d); and

[0049] (e) Define a representative color for each of the resulting k1 clusters.

[0050] (f) Based on k1 representative colors, form the first color palette.

[0051] In this method, the quantity k1 is predefined. The quantity k1 of representative colors can be defined a priori as any desired quantity, but is preferably at least 8, or at least 12, or more preferably at least 16.

[0052] The method for generating the first color palette is as follows: Figure 1-3 As shown in the schematic flowchart, the first step a) of this method includes:

[0053] a) Obtain at least one digital color image with p pixels, each pixel having a color value in an n-dimensional color space.

[0054] The at least one digital color image can be a single image or a collection of images. A single image can be, for example, a combination of multiple color images, such as forming a mood board reflecting a user's personal preferences. Typically, a digital color image is represented by red, green, and blue channels, where each pixel has red, green, and blue color values ​​assigned to it. In a 24-bit digital image, each channel has 8 bits, such that, for example, each pixel can have 256 different red, 256 different green, and 256 different blue color values ​​in the range from 0 to 255. Therefore, the n-dimensional color space can be a three-dimensional RGB space, where the three axes of the RGB space define the red, green, and blue color values. The color value of each pixel can be represented by a vector in a 256×256×256 RGB vector space. It should be understood that the color space can also be another color space, such as CMYK, CIELAB, or CIEXYZ.

[0055] The subsequent steps of this method are:

[0056] b) Ignoring the obtained image, define a predetermined number of K cluster centers, which are distributed along multiple lines in the cubic RGB color space. These multiple lines intersect at the intersection point located at the center of the cubic RGB color space, where K > k1.

[0057] The K initial cluster centers are scattered in the color space. The number of K predefined clusters therefore exceeds the number of k1 representative colors. Since the K initial cluster centers are predefined and distributed in the color space according to a predetermined pattern, the initialization in step b) is non-random, and the method gives reproducible results for a given digital color image. In other words, repeating the method on the same digital color image results in the same set of representative colors.

[0058] Figure 4 This illustrates the pattern of how the K initial clusters are seeded in the color space. Specifically, Figure 4 The diagram illustrates a specific distribution of K predefined cluster centers, which are distributed along multiple straight lines (e.g., 13) in the RGB color space, extending, for example, between opposite corners, rib midpoints, and the center of the plane in the RGB space. These lines intersect at their center points in the RGB space, creating a star-shaped configuration of the predefined cluster centers. Using this star-shaped initialization, the K initial cluster centers are well-distributed in the color space and effectively span the color space observed by the human eye. Preferably, the cluster centers are uniformly distributed in the color space (in a regular manner), i.e., spaced equidistant from each other. This makes the method more likely to identify at least one underrepresented color in a digital color image, i.e., small-scale and / or isolated clusters.

[0059] Step c) of the method involves forming clusters by associating the color value of each pixel with the nearest cluster center. The nearest cluster center can be based on Euclidean distance or any other distance metric. A cluster is defined by the set of pixel color values ​​associated with a single cluster center. Some clusters may be empty when a cluster center has no associated pixel color value.

[0060] In step c1), after clusters are formed, the cluster centers are redefined for each cluster. For example, the redefined cluster centers can be color values ​​that minimize the variance within the cluster, such as the average of the color values ​​in that cluster. Other options for redefined cluster centers include setting the cluster centers to the median, medoid, or other values ​​of the color values ​​in that cluster. The redefined cluster centers can be constituent color values ​​of the cluster, i.e., members of the cluster's color values, but this is not mandatory.

[0061] Step (d) of the method includes reducing the number of clusters to k1 by deleting cluster centers and / or merging clusters, wherein deletion includes:

[0062] - For each cluster, determine the number of pixels associated with that cluster, and

[0063] - Delete a cluster center if the number of pixels associated with it is below a predefined pruning threshold;

[0064] And the merger includes:

[0065] - Determine the distance between two clusters, and

[0066] - Merge clusters whose distance is less than a predefined merging threshold.

[0067] To obtain a predetermined number k1 representative colors, it is desirable to reduce the number of clusters and cluster centers from K to k1. This is accomplished by merging clusters and / or deleting cluster centers. Preferably, cluster deletion and merging are performed. Preferably, a pruning threshold is set to zero so that only the cluster centers of empty clusters that have no pixels in the cluster are deleted. In some embodiments, when the cluster centers of non-empty clusters are removed, pixels previously assigned to that cluster may be marked as unassigned or assigned to the cluster with the closest cluster center. If they are marked as unassigned, they can be assigned to different new clusters in the next iteration. Clusters whose centers are close to each other can be merged because these clusters may represent similar colors. The similarity between clusters can be defined by the distance between their respective cluster centers. The merge threshold can be defined as a distance, where two clusters are merged if the distance between them is less than this threshold distance. The merge and / or pruning thresholds can be adjusted such that the number of clusters is reduced from K to k1.

[0068] The term "distance" used in this paper refers to the similarity or dissimilarity between elements in a color space. It should be understood that any distance metric can be used within the scope of this method to determine the similarity or distance between elements in a color space, such as the 1-norm, 2-norm, 3-norm, ∞-norm, etc. For example, the distance between two color values ​​in a color space can be expressed as the Euclidean distance, i.e., the 2-norm distance. At this point, the cluster center "nearest" to a particular color value of a pixel is the specific cluster center whose distance metric between that color value and any other cluster center is minimized.

[0069] Similarly, the distance between two clusters refers to the similarity or dissimilarity between them. For example, the distance between two clusters can be defined as the distance between their respective cluster centers, or the distance between their respective cluster boundaries. Preferably, the distance between two clusters is the distance between their respective cluster centers.

[0070] Clusters can be merged, for example, by removing one or more cluster centers from a group of clusters that are similar to each other. For instance, two similar clusters can be merged into a single cluster by deleting any one of the cluster centers, such as the one with the minimum or maximum number of pixels associated with it. Optionally, the lowest chromaticity cluster center can be deleted to avoid substantially non-chromatic colors (e.g., black, white, and shades of gray) in the color space. Pixel values ​​associated with the deleted cluster center can be re-associated with any remaining cluster centers. Similar clusters can also be merged by deleting the old cluster centers and defining new cluster centers based on the color values ​​of the old cluster centers and / or similar clusters. The new cluster centers can, for example, be set as the average or weighted average of the similar clusters and / or their respective centers.

[0071] Steps c), c1), and d) can be iterated until a convergence criterion is met. In this embodiment, after step d) has been completed, the method continues with step c) instead of step e). After a certain convergence criterion is met, the method continues with step e) after step d). The convergence criterion can be a predefined number of iterations and / or a convergence measure. The convergence criterion can be reducing the number of clusters from K to k1. Iterating steps c) and c1) is similar to the steps taken in known clustering techniques, such as k-means clustering and similar clustering, where cluster centers converge to local optima. Including step d) in the iteration provides convergence to the number of cluster centers from K predefined clusters to k1 clusters. The number of clusters is iteratively reduced from K to k1 by repositioning the cluster centers in step c1). Cluster centers can be repositioned from their predefined locations (step b) such that the distance between some cluster centers decreases with each iteration. For example, some cluster centers converge to the same local optima, and at some points, the distance between them is defined to be smaller than a predefined merging threshold. In step d), these clusters are merged until k1 clusters are obtained. This method may not converge to k1 clusters. In this case, the pruning threshold and / or merging threshold may need to be set to different values ​​(e.g., manually).

[0072] After reducing the number of clusters in step d), the method includes the following steps:

[0073] e) Define the corresponding representative color for each of the resulting k1 clusters.

[0074] The representative color can be, for example, the average color value of the cluster or the cluster center. Alternatively, the representative color can be a component color, i.e., the color value of the p pixels that are the members of the original digital color image. In this case, the pixel color value closest to the average color value of the cluster (or the cluster center) can be selected, or alternatively, the median or mean of the cluster can be used to define the representative color of the cluster.

[0075] Finally, in step (f), the k1 representative colors defined form the first color palette.

[0076] As a result of step (1), a first color palette is generated. When the described method is implemented on a computer or a mobile device with a screen, preferably, the color palette is hidden from the user (not displayed on the screen).

[0077] In step (2), a second color palette with k2 colors is provided. The second color palette may, for example, contain colors representing a specific design style (modern, industrial, romantic, etc.) or specific words or phrases (light, hazy, bright sky). In this case, the second color palette can be obtained from a digital color image representing the corresponding design style, word, or phrase. Suitablely, the second color palette can be generated from a second mood board containing one or more digital color images. Preferably, the second color palette is generated from the second mood board using the algorithm described above that takes into account the first color palette with emphasized colors.

[0078] In other embodiments, the second color palette is not derived from a mood board, but rather is an existing color palette, such as one curated by a paint manufacturer or compiled by a designer. An example of such a palette is one that includes trendy colors for a particular year, such as AkzoNobel's Sikkens ColorFutures. TM .

[0079] The number of colors k2 in the second color palette is not required and any actual number can be used. It can be the same number as k1, or a lower or higher number. In some embodiments, the number of colors k2 is preferably at least 8, or at least 12, or more preferably at least 16. In other embodiments, the number of colors k2 can be less than 8, for example, 5 or less. The number of colors k2 can also be 1, meaning that the second color palette consists of only one color.

[0080] In step (3), the colors in the first color palette are sorted according to a scoring algorithm that assigns a score to each color in the first color palette based on the color difference between each color in the first color palette and each color in the second color palette. In this step, the colors in the first color palette are compared with the colors in the second color palette. This is done using the scoring algorithm.

[0081] Any suitable scoring algorithm can be used that allows the set of colors to be compared with each other and to generate a score assigned to each color. Preferably, the scoring algorithm includes the following steps: for each color in the first color palette, calculating the color difference with each color in the second color palette, determining the minimum color difference from all obtained color differences, and assigning a score to the color from the first color palette based on the minimum color difference, thereby assigning scores to all colors in the first color palette.

[0082] Color difference is represented as a value. The color difference between two colors can be based on the distance between these colors in an n-dimensional color space. The integer n can be any possible integer, such as 1, 2, 3, and larger numbers, such as 6, 9, 12. For example, a three-dimensional RGB space or a CIELAB space can be used, preferably CIELAB. It should be understood that the color space can also be another color space such as HSV or HSL. Known algorithms for calculating color difference can be used, such as the CIEDE2000 color difference formula. Color difference is not limited to a multi-dimensional color space. Color difference also includes differences in brightness or chromaticity, which represent the corresponding dimension in a multi-dimensional color space. For example, chromaticity (C) is a dimension in the 3-dimensional CIE-LCh space. Brightness (L) is a dimension from the 3-dimensional CIE-LAB space (or Y from the CIE-XYZ space).

[0083] When all colors in the first color palette receive scores based on the aforementioned criteria, the colors in the first color palette are sorted according to their scores, for example, from lowest to highest score or vice versa. Depending on the scoring algorithm used, the highest score could, for example, represent the smallest color difference with the second color palette. The color with the highest score will represent the color with the lowest color difference with the second color palette.

[0084] Therefore, in step (4), k is selected from the first color palette. sub A subset of colors that has the minimum color difference (e.g., the highest score) with respect to colors in a second color palette, wherein the subset of colors is a color sub-palette. The number k sub This corresponds to the desired number of colors in the final color sub-palette, and is preferably a predefined number. The number of colors, k. sub Typically less than 8, preferably less than 5, more preferably less than 2, 3, or 4. In any case, the number of colors k sub It is less than the number of colors k1 in the first color palette.

[0085] For example, a first color palette might contain 16 colors, while a second color palette might contain 9 colors. First, the color difference between the first color in the 16-color palette and all the colors in the second color palette (which have 9 colors) is calculated, resulting in 9 color difference values ​​for the first color. Then, the assigned score for this color is the lowest color difference, which is the color difference between the first color in the 16-color palette and its closest match in the 9-color palette. This process then continues to the second color in the 16-color palette, repeating the comparison with all 9 colors in the second color palette, calculating and assigning a score to the second color in the 16-color palette. This process is repeated for all 16 colors, ending with 16 scores, one score per color. The final 4-color palette is determined by selecting the 4 colors with the lowest scores from the 16-color palette.

[0086] In some embodiments, the second color palette may contain only one color, such as blue. In this case, the target sub-palette will contain colors with the lowest color difference score from that particular color, such as the bluest color from the first color palette.

[0087] As a further step, alternatively, the colors in the obtained sub-palette can be compared with and converted to standard paint colors (e.g., commercially available paints). This can be achieved by replacing the colors in the sub-palette with the most similar standard paint color. Color differences can be calculated in the same manner as described above. The advantage of this is that the user obtains a colored sub-palette that only displays colors available on the market, such as wall paint or wood varnish.

[0088] In one embodiment, in the method of selecting paint colors, each color of the color sub-palette is replaced by the most similar paint color in the range of paint colors available on the market.

[0089] When the described method is implemented on a computer or a mobile device with a screen, the color sub-palette obtained in step (4) is preferably displayed to the user on the screen. Any suitable color screen conventionally used with computers and mobile devices can be used.

[0090] After obtaining the color sub-palette in step (4), paint can be prepared for any of the colors contained in the sub-palette. This can be done, for example, using paint mixing equipment, as is common in the paint industry.

[0091] The present invention also provides a data processing apparatus comprising means for performing the steps of the above-described method. The method can be performed on a data processing device such as a point-of-sale computer system or a mobile computing system including a display, for example, a smartphone, tablet computer, laptop computer, etc. The display is preferably a color display.

[0092] The present invention also provides a computer program product including instructions that, when executed by a computer, cause the computer to perform the steps of the method of the present invention. The computer program product may be an application or web-based software loaded and executed on a general-purpose computer or mobile computing system.

[0093] In one embodiment, the computer program causes the computer to output an identifier for each color in the color sub-palette, which is used for the color of the paint. For example, the identifier could be a code or name that identifies a color from a range of paint colors.

[0094] In one embodiment, the computer program will cause the computer to output a link to an ordering platform from which paint of at least one color from a color sub-palette can be ordered. The ordering platform may be, for example, in a mobile computing system application or a web browser. The ordering platform may also be, for example, an online store.

[0095] It should be understood that all features and options mentioned in connection with this method also apply to the system and computer program product, and vice versa. It will also be clear that any one or more of the foregoing aspects, features, and options can be combined.

[0096] In the claims, any reference numerals in parentheses should not be construed as limiting the claims. The word "comprising" does not exclude the presence of features or steps other than those listed in the claims. Furthermore, the words "a" and "an" should not be construed as limited to "only one," but are used to mean "at least one," and do not exclude multiples. The fact that certain measures are referenced in mutually different claims does not mean that a combination of these measures cannot be used for advantageous purposes.

Claims

1. A method for obtaining a color sub-palette from a first mood board comprising one or more digital color images, the method being performed on a mobile device or computer having a screen and comprising the following steps: (1) Generate a first color palette with k1 representative colors based on the first mood palette. (2) Provide a second color palette with k2 colors. (3) Sort the colors in the first color palette according to a scoring algorithm, wherein the scoring algorithm assigns a score to each color in the first color palette based on the color difference between each color in the first color palette and each color in the second color palette, and (4) Select k from the first color palette sub A subset of colors, the subset having a fraction corresponding to the minimum color difference with a color in the second color palette, the subset of colors being the color sub-palette, and the color sub-palette being displayed on the screen. The first color palette is obtained from the first mood board through a method including the following steps: (a) Obtain at least one digital color image having p pixels, each pixel having a color value in an n-dimensional color space; (b) Ignoring the obtained image, define a predetermined number of K cluster centers, which are distributed along multiple lines in the cubic RGB color space, and the multiple lines intersect at the intersection point located at the center of the cubic RGB color space, where K > k1; (c) Clustering is formed by associating the color value of each pixel with the nearest cluster center; and c1) After the clusters are formed, the cluster centers are redefined for each cluster; (d) Reduce the number of clusters to k1 by deleting cluster centers and / or merging clusters, where deletion includes: - For each cluster, determine the number of pixels associated with that cluster, and - If the number of pixels associated with a cluster center is lower than a predefined pruning threshold, delete the cluster center; And the merger includes: - Determine the distance between two clusters, and -Merge clusters whose distance is less than a predefined merging threshold; Among them, iterative steps c), c1), and d); and (e) Define a representative color for each of the resulting k1 clusters. (f) Based on k1 representative colors, form the first color palette.

2. The method according to claim 1, wherein, The second color palette is generated based on a second mood board containing one or more digital color images.

3. The method according to claim 2, wherein, The digital color images in the second color palette represent design styles, words, or phrases.

4. The method according to claim 1, wherein, The designer selects the colors from the second color palette.

5. The method according to any one of claims 1 to 4, wherein, The scoring algorithm includes the following steps: for each color in the first color palette, calculate the color difference with each color in the second color palette, determine the minimum color difference based on all obtained color differences, and assign scores to the colors from the first color palette based on the minimum color difference, thereby assigning scores to all colors in the first color palette.

6. The method according to any one of claims 1 to 4, wherein, k1 is at least 12.

7. The method according to any one of claims 1 to 4, wherein, k sub It is 5 or smaller.

8. A method for selecting paint colors, comprising the method according to any one of claims 1 to 7, and further comprising selecting a paint color corresponding to each color of the color sub-palette.

9. The method according to claim 8, wherein, Each color in the color sub-paintbrush is replaced with the most similar paint color available from the range of commercially available paint colors.

10. A data processing apparatus comprising means for performing the steps of the method according to any one of claims 1 to 9.

11. A computer program product comprising instructions that, when executed by a computer, cause the computer to perform the steps of the method according to any one of claims 1 to 9.

12. The computer program product of claim 11, which causes the computer to output an identifier for each color of the color sub-palette, the identifier being for the color of the paint.

13. The computer program product of claim 11, wherein the computer program product causes the computer to output a link to an ordering platform from which paint of at least one color from the color sub-palette can be ordered.

Images