Selective Feature Fusion Based Adaptive Image Segmentation Algorithm

Abstract

Image segmentation is an essential task in computer vision and pattern recognition. There are two key challenges for image segmentation. One is to find the most discriminative image feature set to get high-quality segments. The other is to achieve good performance among various images. In this paper, we firstly propose a selective feature fusion algorithm to choose the best feature set by evaluating the results of presegmentation. Specifically, the proposed method fuses selected features and applies the fused features to region growing segmentation algorithm. To get better segments on different images, we further develop an algorithm to change threshold adaptively for each image by measuring the size of the region. The adaptive threshold can achieve better performance on each image than fixed threshold. Experimental results demonstrate that our method improves the performance of traditional region growing by selective feature fusion and adaptive threshold. Moreover, our proposed algorithm obtains promising results and outperforms some popular approaches.

Segmentation of the Fabric Pattern Based on Improved Fruit Fly Optimization Algorithm

“…It has a direct impact on the accuracy of drawing, hemming, and cloth printing [4]. In actual production, new patterns can be formed by colouring the split pattern, so as to enrich the variety of fabric products [5][6][7][8]. At present, there are many common image segmentation methods, for example, segmentation methods based on the edge-extraction operators of Canny and Sobel and segmentation methods based on the clustering analysis of mean shift [9][10][11][12].…”