Intelligent classification method for colors of solid wood boards

Abstract

The invention discloses an intelligent classification method for colors of solid wood boards. The method comprises the following steps: preprocessing solid wood board pictures; converting an RGB color space into a Lab color space and an HSV color space; obtaining a first moment and a second moment of colors of the Lab color space and the HSV color space; clustering the pictures by using a K-Means clustering algorithm; performing main color extraction on the solid wood board pictures after low-pass filtering by adopting a main color extraction method based on a K-Means clustering algorithm, and performing grade division according to the main color content; performing high-pass filtering on the pictures to obtain texture information, and dividing the pictures into straight grains or curved grains; labeling the solid wood board pictures, and making a sample set; and inputting the to-be-classified solid wood board pictures into an optimal color classification model obtained by training and verification to realize color classification of a solid wood board. According to the method, the colors of the solid wood board pictures can be effectively classified, the method does not need color space quantization, and the feature vector dimensionality is low.

Description

technical field [0001] The invention belongs to the technical field of image recognition, and in particular relates to an intelligent color classification method for solid wood boards. Background technique [0002] Color moments are a simple and effective representation method of color features. There are first-order moments (mean, mean), second-order moments (variance, variance) and third-order moments (slope, skewness), etc. Since color information is mainly distributed in low-order Moments, so the first-order moment, second-order moment and third-order moment are sufficient to express the color distribution of the image, and the color moment has been proved to be effective in representing the color distribution in the image. [0003] Color is one of the most important contents of color images and is widely used in image retrieval. However, when extracting color features from an image, many algorithms must first quantify the image. Quantization processing is easy to caus...

AI Technical Summary

Problems solved by technology

Quantization processing can easily lead to false detection, and the generated image feature dimension is high, which is not conducive to retrieval

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