Cotton foreign fiber characteristic selection method based on particle swarm optimization algorithm

Abstract

The invention discloses a cotton foreign fiber characteristic selection method based on a particle swarm optimization algorithm and relates to the technical field of image processing. The cotton foreign fiber characteristic selection method comprises the following steps of: S1, initializing a particle swarm according to characteristic data of a characteristic training sample set obtained by characteristic extraction; S2, designing an SVM (Support Vector Machine) classifier according to the sample set; S3, classifying the sample set and calculating a fitness value of a particle; S4, optimally solving the fitness value of the current particle and comparing a globally optimal solution of a group; and updating a locally optimal solution and the globally optimal solution; S5, calculating the movement speed and new position of the particle; and S6, if a finishing condition is met, finishing and outputting an optimal characteristic set; otherwise, adding 1 into an iteration and returning to the step S2. According to the method disclosed by the invention, the optimal selection can be carried out on a cotton foreign fiber characteristic, and to the method adapts to the requirements of the SVM classifier; and furthermore, the classifying performance is further improved.

Description

technical field [0001] The invention relates to the technical field of image processing, in particular to a method for selecting cotton heterosexual fiber features based on a particle swarm optimization algorithm. Background technique [0002] Foreign fibers in cotton refer to non-cotton fibers and colored fibers that are mixed into cotton during the process of cotton picking, tanning and purchasing, which have a serious impact on the quality of cotton and its products, such as chemical fibers, hair, hemp rope, etc. Although the content of foreign fibers in lint is small, it has a serious impact on the quality of cotton textiles. Foreign fibers mixed into the leather surface are likely to break the cotton yarn and reduce production efficiency; when weaving, it affects the quality of the cloth surface; when dyeing, it affects the appearance and causes great harm to the quality of cotton yarn and cloth surface. In our country, at present, many cotton spinning processing enter...

AI Technical Summary

Problems solved by technology

However, existing research results have shown that the performance of SVM classifiers will decrease when classifying redundant and irrelevant data sets. Therefore, feature optimization enables it to adapt to the requirements of SVM classifiers and remove redundant and irrelevant feature data sets. In order to further improve the classification performance, it has become an important research part in machine learning.

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