Similar image colorization algorithm based on classification learning

Abstract

The invention discloses a similar image colorization algorithm based on classification learning. The similar image colorization algorithm comprises the following steps: sample images are collected, an image gradation co-occurrence matrix attribute is extracted, the sample images are classified into five categories through the AP algorithm, superpixels of a target image and superpixels of a reference image are calculated respectively, then, colors are transferred from the reference image to the target image, colors of the superpixels are corrected afterwards according to continuity of image space, and finally the algorithm is used for conducting color diffusion to complete colorization. According to the similar image colorization algorithm, the influence on an image by a global attribute of the image is considered, the image gradation co-occurrence matrix attribute is extracted to conduct classification learning on parameters of a superpixel matching function, as a result, different parametric functions can be provided for superpixel matching on images with different compositions, and the universality of the similar image colorization algorithm on the images is improved; besides, after the matching process, region growing algorithm partition can be conducted at a superpixel level, and color correction can be conducted in a region.

Description

【Technical field】 [0001] The invention relates to the technical field of similar image colorization algorithms, in particular to the technical field of similar image colorization algorithms based on classification learning. 【Background technique】 [0002] The goal of image colorization is to add color to the grayscale image so that the colorized image has perceptual meaning and visual appeal. But the crux of the colorization problem is that there are many potential colors that can be assigned to the pixels of the target grayscale image (eg, leaves can be yellow, green, and brown). So the colorization problem is a problem for which there is no absolute correct solution. [0003] To reduce the impact of potential color assignments, human interaction plays an important role in the colorization process. The interactive colorization method requires the user to manually mark the color of the target image, and then smoothly spread the manually marked color value to the entire ima...

AI Technical Summary

Problems solved by technology

So the colorization problem is a problem for which there is no absolute correct solution

In most cases, although these methods can reduce the time spent on manual labeling, these methods often require careful tuning of a large number of parameters to obtain satisfactory results.

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