Clustering Optimized Portrait Matting Algorithm Based on Improved Sparrow Algorithm

Abstract

As a result of the influence of individual appearance and lighting conditions, aberrant noise spots cause significant mis-segmentation for frontal portraits. This paper presents an accurate portrait segmentation approach based on a combination of wavelet proportional shrinkage and an upgraded sparrow search (SSA) clustering algorithm to solve the accuracy challenge of segmentation for frontal portraits. The brightness component of the human portrait in HSV space is first subjected to wavelet scaling denoising. The elite inverse learning approach and adaptive weighting factor are then implemented to optimize the initial center location of the K-Means algorithm to improve the initial distribution and accelerate the convergence speed of SSA population members. The pixel segmentation accuracy of the proposed method is approximately 70% and 15% higher than two comparable traditional methods, while the similarity of color image features is approximately 10% higher. Experiments show that the proposed method has achieved a high level of accuracy in capricious lighting conditions.

Keywords

• elite backward learning strategy
• K-means
• portrait segmentation
• sparrow search algorithm
• wavelet proportional shrinkage