Segmentation method for foreign fibers in cotton based on improved simple linear iterative clustering algorithm and SVM

Abstract

In response to the challenge of detecting small foreign fibers mixed in cotton during picking and transportation, a segmentation method for identifying foreign fibers in cotton based on superpixel features and a support vector machine (SVM) is proposed. The method involves integrating the local multidirectional anti-noise binary pattern with the simple linear iterative clustering algorithm to segment the image into superpixel units with similar color features. Color features and grayscale histogram texture features of each superpixel block are extracted to form a multidimensional feature vector. The particle swarm optimization–SVM classification model is then utilized to distinguish between background superpixels and foreign fiber superpixels. Finally, superpixel correction is performed based on confidence and regional adjacency relations, with the introduction of the Bhattacharyya coefficient, to obtain a completely segmented image. The superpixel classification accuracy is reported to be 98.65%. Compared with traditional image segmentation algorithms, this proposed algorithm shows superior segmentation performance.