Preprocessing optimization method for image color classification

Abstract

The invention discloses a preprocessing optimization method for image color classification. The method comprises the steps of carrying out the statistics of the number of gray values in RGB channels in an image, and redefining points with the gray values of 0 and 255 for color gradation adjustment, conducting gaussian filtering and sharpening on the gray-scale maps of the three channels, updating the RGB three-dimensional vector of each point in the sharpened color image according to the offset vector, and completing the preprocessing optimization of the image. According to the image color classification method subjected to preprocessing optimization, after the preprocessing optimization method for image color classification is used, a K-means++ algorithm is used for carrying out color classification on the image. According to the method, the gray values of all the points are updated through color gradation adjustment, and the influence of gradient colors between two colors with large difference on the classification effect is reduced through convolution operation, so that the classification color difference of the optimized picture is larger and brighter, and the color classification accuracy is improved during image color classification.

Description

technical field [0001] The invention relates to the technical field of digital image processing and application, in particular to a preprocessing optimization method for image color classification. Background technique [0002] As the visual basis for human perception of the world, images are an important means for humans to obtain information, express information and transmit information. In essence, an image is also a form of expression of data. The image is represented as a digital matrix inside the computer, and the value of each element in the matrix is ​​called a pixel value. In the computer, images can be divided into basic types such as binary images, grayscale images and RGB images according to the amount of color and grayscale. The digital matrix of a binary image has only two values, namely "0" for white and "1" for black. Thus, the contrast of light and dark presents different image appearances. The difference between a grayscale image and a binary image is th...

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Problems solved by technology

[0004] For this reason, the technical problem to be solved by the present invention is to overcome the deficiencies in the prior art and propose a preprocessing optimization method for image color classification, mainly when the overall image color is not clear and there are a large number of gradient colors in the image Can optimize the vividness of colors and improve color classification results

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