CN109087364B - Method for predicting color harmony degree according to mode - Google Patents
Method for predicting color harmony degree according to mode Download PDFInfo
- Publication number
- CN109087364B CN109087364B CN201810894794.9A CN201810894794A CN109087364B CN 109087364 B CN109087364 B CN 109087364B CN 201810894794 A CN201810894794 A CN 201810894794A CN 109087364 B CN109087364 B CN 109087364B
- Authority
- CN
- China
- Prior art keywords
- color
- pairs
- harmony
- score
- theme
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/90—Determination of colour characteristics
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/10—Segmentation; Edge detection
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10024—Color image
Landscapes
- Engineering & Computer Science (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Color Image Communication Systems (AREA)
- Spectrometry And Color Measurement (AREA)
- Image Processing (AREA)
Abstract
The invention discloses a method for predicting color harmony degree according to a mode. The method is based on a large amount of data sets, according to the input color subjects of five different colors, the color pair score obtained after the color subjects are divided by the five color subjects is obtained through a mode method, then the color pairs are replaced to the five color subjects, and the predicted harmony score is obtained through the mode method, so that the concept of harmony of colors is quantized into the score. The method can meet the requirement that the formed color table is diversified, the obtained harmony score has high accuracy, and meanwhile, the method can be used in scenes with a plurality of color collocation in real life.
Description
Technical Field
The invention belongs to the technical field of image processing, and particularly relates to a method for predicting color harmony degree according to a mode.
Background
Color matching plays an indispensable role in many aspects of life. With the development of multimedia technology and software design technology, designers can rapidly design color theme schemes with different styles aiming at different object groups, and can not distinguish and manually extract collocate colors through professional knowledge and naked eyes like the prior art, so that the efficiency is greatly improved. However, how to grasp the harmony degree of the colors needing to be matched and the harmony degree of the colors are always a problem to be solved. Since the harmony degree of the colors directly affects the color matching result, it is important to find a method capable of quantifying and predicting the concept of the harmony degree of the colors.
The traditional color harmony prediction method is based on a color harmony theory, analyzes some simple color characteristics such as hue, brightness, chroma and the like in a small amount of sample colors by a mathematical modeling method, and then calculates the harmony value by a formula. Such a method obtains relatively single results because of fewer color features to consider. In recent years, researchers train a data set with a large number of color features to achieve a prediction effect through a LASSO regression method based on big data in machine learning. Although the method gets rid of the defect of single result, the experimental result becomes more objective and more robust through the training of big data. However, the harmony of the predictions of this method is not accurate enough. Therefore, it is very important to find a prediction method capable of representing color harmony more accurately.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides a method for predicting the color harmony degree according to the mode.
The method specifically comprises the following steps:
the method comprises the following steps: color themes obtained by crowdsourcing under Amazon Mechanical Turk are adopted, and each color theme is formed by combining five different color blocks. In the color theme of different five colors, the color relationship between each other is obtained based on two adjacent color pairs. Therefore, all the color themes in the data set are divided into pairwise adjacent color pairs, that is, a five-color theme contains four divided pairwise adjacent color pairs.
Step two: and assigning the color and the harmony score corresponding to each color theme in the data to the corresponding four color pairs, namely assigning the scores of the original five-color theme to the four different segmented color pairs respectively, wherein the scores of the four color pairs are the same.
Step three: all color pairs are arranged according to the positive sequence of the color components in the RGB color space, then the color pairs in which the color components are repeated are removed and the harmony scores of the color pairs are kept, so that the remaining color pairs after the deduplication contain all the same scores as the color components of the color pairs before the deduplication.
Step four: and carrying out mode operation on all the color pairs, namely extracting the value with the highest occurrence number of the harmony score in each color pair. r isi pairAs a new score for the ith color pair, n represents the number of repeated color pairs and the harmony score:
ri pair＝mode(r1,r2,r3,…,rn)
step five: all color pairs and their harmonious values ri pairSubstituting the positions of the original five-color themes into the color themes to enable each five-color theme to contain four same color pairs and corresponding harmony scores of four different color pairs.
Step six: taking out the harmony score with the most occurrence times in the five-color theme as the final score r 'of the jth color theme by a mode value taking method'jThereby, the prediction effect is achieved:
the invention has the beneficial effects that: compared with the prior art, the method adopts a totally different mode-based method in statistics to solve the problems of color harmony prediction, greatly improves the accuracy of the result and reduces the error.
Detailed Description
The present invention will be further illustrated by the following examples
The technical scheme adopted by the invention for solving the technical problems is as follows:
the method comprises the following steps: the invention adopts color themes obtained by crowdsourcing under Amazon Mechanical Turk, and each color theme is formed by combining five different color blocks. Research shows that in the color theme of different five colors, the mutual color relationship is obtained based on two adjacent color pairs. Therefore, all the color themes in the data set are divided into pairwise adjacent color pairs, that is, a five-color theme contains four divided pairwise adjacent color pairs.
Step two: and assigning the color and the harmony score corresponding to each color theme in the data to the corresponding four color pairs, namely assigning the scores of the original five-color theme to the four different segmented color pairs respectively, wherein the scores of the four color pairs are the same.
Step three: all color pairs are arranged according to the positive sequence of the color components in the RGB color space, then the color pairs in which the color components are repeated are removed and the harmony scores of the color pairs are kept, so that the remaining color pairs after the deduplication contain all the same scores as the color components of the color pairs before the deduplication.
Step four: and carrying out mode operation on all the color pairs, namely extracting the value with the highest occurrence number of the harmony score in each color pair. r isi pairAs a new score for the ith color pair, n represents the number of repeated color pairs and the harmony score:
ri pair＝mode(r1,r2,r3,…,rn)
step five: all colors are paired and summedValue r of harmonic degreei pairSubstituting the positions of the original five-color themes into the color themes to enable each five-color theme to contain four same color pairs and corresponding harmony scores of four different color pairs.
Step six: taking out the harmony score with the most occurrence times in the five-color theme as the final score r 'of the jth color theme by a mode value taking method'jThereby, the prediction effect is achieved:
to illustrate the effectiveness of the present invention, the Mean Square Error (MSE) and Mean Absolute Error (MAE), as well as the pearson correlation coefficient R (correlation), were used to measure the experimental results and compared to the predicted results generated by LASSO regression. The final results are shown in the following table:
TABLE 1 comparison of color harmony prediction results
| MAE | MSE | R | |
| Mode prediction result | 0.0227 | 0.2435 | 78.69％ |
| LASSO prediction results | 0.0518 | 0.4456 | 73.61％ |
In conclusion, the invention finally obtains an interval score to describe the harmony degree by inputting a group of multi-color-block color themes with different colors, thereby achieving the purpose of quantifying the fuzzy concept of color harmony and enabling a user to intuitively distinguish the harmony degree of the colors through the score.
Claims (1)
1. A method for predicting the degree of harmony of colors according to the mode, the method comprising the steps of:
the method comprises the following steps: adopting color themes obtained by crowdsourcing under Amazon Mechanical Turk, wherein each color theme is formed by combining five different color blocks; in the color subjects of different five colors, the color relationship among the colors is obtained on the basis of pairwise adjacent color pairs; therefore, all color themes in the data set are divided into pairwise adjacent color pairs, namely, a five-color theme contains four divided pairwise adjacent color pairs;
step two: assigning the color and harmony score corresponding to each color theme in the data to corresponding four color pairs, namely assigning the scores of the original five-color theme to the four different segmented color pairs respectively, wherein the scores of the four color pairs are the same;
step three: arranging all color pairs according to the positive sequence of color components in an RGB color space, then removing the color pairs in which the color components are repeated, and keeping the harmony scores thereof;
step four: performing mode operation on all color pairs, namely extracting a value with the highest occurrence frequency of the harmony score in each color pair; r isi pairAs a new score for the ith color pair, n represents the number of repeated color pairs and the harmony score:
ri pair＝mode(r1,r2,r3,...,rn)
step five: all color pairs and their harmonious values ri pairSubstituting the positions of the original five-color themes into the color themes to enable each five-color theme to contain four same color pairs and corresponding harmony scores of four different color pairs;
step six: taking out the harmony score with the most occurrence times in the five-color theme as the final score r of the jth color theme by a mode value taking methodj', thereby achieving a predictive effect:
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810894794.9A CN109087364B (en) | 2018-08-08 | 2018-08-08 | Method for predicting color harmony degree according to mode |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810894794.9A CN109087364B (en) | 2018-08-08 | 2018-08-08 | Method for predicting color harmony degree according to mode |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN109087364A CN109087364A (en) | 2018-12-25 |
| CN109087364B true CN109087364B (en) | 2021-11-12 |
Family
ID=64833837
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201810894794.9A Active CN109087364B (en) | 2018-08-08 | 2018-08-08 | Method for predicting color harmony degree according to mode |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109087364B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109903349B (en) * | 2019-01-28 | 2021-07-02 | 浙江工商大学 | Color harmony prediction method based on maximum likelihood estimation |
Family Cites Families (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US8085276B2 (en) * | 2006-11-30 | 2011-12-27 | Adobe Systems Incorporated | Combined color harmony generation and artwork recoloring mechanism |
| US10026192B2 (en) * | 2013-10-18 | 2018-07-17 | Ford Global Technologies, Llc | Color harmony verification system |
| CN106775747B (en) * | 2016-12-28 | 2020-03-24 | Tcl集团股份有限公司 | Color configuration method and device |
| CN107390976B (en) * | 2017-07-26 | 2020-09-08 | 上海展扬通信技术有限公司 | Icon display processing method and electronic equipment |
-
2018
- 2018-08-08 CN CN201810894794.9A patent/CN109087364B/en active Active
Also Published As
| Publication number | Publication date |
|---|---|
| CN109087364A (en) | 2018-12-25 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Lu et al. | Cross-modality person re-identification with shared-specific feature transfer | |
| Zhong et al. | Research on deep learning in apple leaf disease recognition | |
| Yu et al. | A unified pruning framework for vision transformers | |
| Xie et al. | On data scaling in masked image modeling | |
| CN110120080B (en) | Method for rapidly generating main color of standard camouflage color | |
| Kong | Image retrieval using both color and texture features | |
| CN107016415A (en) | A kind of coloured image Color Semantic sorting technique based on full convolutional network | |
| CN109087364B (en) | Method for predicting color harmony degree according to mode | |
| CN108304920A (en) | A method of multiple dimensioned learning network is optimized based on MobileNets | |
| CN106530361A (en) | Color correction method for color face image | |
| Gao et al. | Vacl: Variance-aware cross-layer regularization for pruning deep residual networks | |
| CN113807176B (en) | Small sample video behavior recognition method based on multi-knowledge fusion | |
| CN110569957A (en) | Optimization method based on artificial bee colony algorithm | |
| Gu et al. | Leaf species recognition based on VGG16 networks and transfer learning | |
| CN113505856A (en) | Hyperspectral image unsupervised self-adaptive classification method | |
| CN110163855B (en) | Color image quality evaluation method based on multi-path deep convolutional neural network | |
| CN107767367A (en) | It is a kind of for HDR figures without reference mass method for objectively evaluating | |
| CN107705299B (en) | Image quality classification method based on multi-attribute features | |
| CN117392450A (en) | Steel material quality analysis method based on evolutionary multi-scale feature learning | |
| Wang et al. | The research of ear identification based on improved algorithm of moment invariant | |
| Liang et al. | Research on Improved K-means Image Segmentation Algorithm Based on HSV Space | |
| Wang et al. | psoResNet: An improved PSO-based residual network search algorithm | |
| AU2021101210A4 (en) | An Endmember Extraction Method Of Hyperspectral Image | |
| Utami et al. | The EfficientNet Performance for Facial Expressions Recognition | |
| Li et al. | Review of tobacco leaf classification research based on artificial intelligence |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |