An improved K-means algorithm for underwater image background segmentation

Wei Chen,Meiying Zhang,Siyu Chen,Cenyu He,Chunlin Ji

doi:10.1007/s11042-021-10693-7

Abstract

Conventional algorithms fail to obtain satisfactory background segmentation results for underwater images. In this study, an improved K-means algorithm was developed for underwater image background segmentation to address the issue of improper K value determination and minimize the impact of initial centroid position of grayscale image during the gray level quantization of the conventional K-means algorithm. A total of 100 underwater images taken by an underwater robot were sampled to test the aforementioned algorithm in respect of background segmentation validity and time cost. The K value and initial centroid position of grayscale image were optimized. The results were compared to the other three existing algorithms, including the conventional K-means algorithm, the improved Otsu algorithm, and the Canny operator edge extraction method. The experimental results showed that the improved K-means underwater background segmentation algorithm could effectively segment the background of underwater images with a low color cast, low contrast, and blurred edges. Although its cost in time was higher than that of the other three algorithms, it none the less proved more efficient than the time-consuming manual segmentation method. The algorithm proposed in this paper could potentially be used in underwater environments for underwater background segmentation.

Highlights

The significance of ocean exploration has been highlighted by researchers [19]
This study proposes an improved K-means algorithm in underwater image background segmentation
The comparative results regarding the foreground masks obtained by the improved Kmeans algorithm and other methods are presented in Fig. 11 below, where the capacity of the improved K-means algorithm to extract the foreground mask is demonstrated (Fig. 11d)

Summary

Introduction

Within the field of ocean exploration, underwater detection technology for identifying objects in underwater environments is of high significance from a number of security and recovery perspectives, and has become a topic for extensive research. J 1⁄4 ∑ ∑ dist xij; m j ð1Þ j1⁄41 i1⁄41 where dist (xij, mj) represents the distance between the data point xij and the cluster centroid mj. The purpose of this calculation is to find a classification method for a set of centroids where the sum of data in each group and the centroid of the group are minimized.

Methods

Results

Conclusion