Color information interactive instant extracting method based on mobile terminal

A color information and mobile terminal technology, applied in character and pattern recognition, instruments, computer parts, etc., can solve the problems of lack of convenient tools for color extraction and application

Inactive Publication Date: 2018-11-02
句容市宝启电子科技有限公司
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  • Summary
  • Abstract
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  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Mobile terminals such as mobile phones now have camera functions, and it is very convenient to c

Method used

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  • Color information interactive instant extracting method based on mobile terminal

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Embodiment Construction

[0014] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0015] Such as figure 1 As shown, the present invention provides a method for interactive instant extraction of color information based on a mobile terminal, comprising the following steps:

[0016] Step 1, read image color data;

[0017] Step 2, the user inputs the number of color categories;

[0018] Step 3, select the initial cluster center;

[0019] Step 4, calculate the distance between the color of each pixel and each cluster center, and classify it into the category to which the nearest cluster center belongs;

[0020] Step 5, define the mean square error as the measurement function, and calculate the measurement function value of each type of color (Reference: Wang Jing. Research on Image Classification Algorithm Based on Color Features [D]. Dalian: Dalian Maritime University, 2012), if it does not converge , then calculate the RGB mean valu...

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Abstract

The invention discloses a color information interactive instant extracting method based on a mobile terminal. The method comprises the following steps: step one, reading image color data; step two, inputting color category number by a user; step three, selecting an initial clustering center; step four, computing the distance between the color of each pixel and each clustering center, and classifying the distance into the category to which the nearest clustering center belongs; and step five, defining mean square error as a measure function, computing the measure function value of each categoryof color, and computing the RGB mean value of each category of color if the measure function value is not convergent, and updating the clustering center by taking the RGB mean value, and performing the next turn of clustering until the measure function convergence or cycle frequency exceeds the maximum set frequency.

Description

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Claims

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Application Information

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Owner 句容市宝启电子科技有限公司
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