Image Segmentation by Searching for Image Feature Density Peaks

Zhe Sun,Yunlong He,Meng Qi,Yuanjie Zheng,Weikuan Jia,Wei Zou,Jian Lian,Hong Liu

doi:10.3390/app8060969

Zhe Sun, Yunlong He + Show 6 more

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https://doi.org/10.3390/app8060969

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Abstract

Image segmentation attempts to classify the pixels of a digital image into multiple groups to facilitate subsequent image processing. It is an essential problem in many research areas such as computer vision and image processing application. A large number of techniques have been proposed for image segmentation. Among these techniques, the clustering-based segmentation algorithms occupy an extremely important position in this field. However, existing popular clustering schemes often depends on prior knowledge and threshold used in the clustering process, or lack of an automatic mechanism to find clustering centers. In this paper, we propose a novel image segmentation method by searching for image feature density peaks. We apply the clustering method to each superpixel in an input image and construct the final segmentation map according to the classification results of each pixel. Our method can give the number of clusters directly without prior knowledge, and the cluster centers can be recognized automatically without interference from noise. Experimental results validate the improved robustness and effectiveness of the proposed method.

Highlights

Image segmentation refers to partition an image into distinctive regions, where each region consists of pixels with similar attributes
We propose a novel clustering-based method for image segmentation, which can automatically recognize the cluster centers by searching density peaks efficiently without defining the threshold
We proposed a new image segmentation method which does not require a priori knowledge and a large amount of computation

Summary

Introduction

Image segmentation refers to partition an image into distinctive regions, where each region consists of pixels with similar attributes. The purpose of image segmentation is to simplify or change the representation of an image into some common features that are more meaningful and easier to analyze [1,2,3]. Over the past several decades, image segmentation has been widely used in diverse applications of computer vision and image processing, such as object detection [4], face recognition [5], image retrieval [6], and medical image analysis [7]. A large number of techniques and algorithms have been proposed for image segmentation. Color image segmentation of natural and outdoor scene is a well-studied problem due to its numerous applications in computer vision. Different methods have been already proposed in the state of the art

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