# Image Segmentation by Searching for Image Feature Density Peaks  
[PERSON]  
1 School of Information Science and Engineering, Shandong Normal University, Jinan 25030, China; [EMAIL_ADDRESS] (Z.S.); [EMAIL_ADDRESS] (M.Q.); [EMAIL_ADDRESS] (Y.H.); [EMAIL_ADDRESS] (H.L.)  
[PERSON]  
1 School of Information Science and Engineering, Shandong Normal University, Jinan 25030, China; [EMAIL_ADDRESS] (Z.S.); [EMAIL_ADDRESS] (M.Q.); [EMAIL_ADDRESS] (Y.H.); [EMAIL_ADDRESS] (H.L.)  
[PERSON]  
2 Department of Electrical Engineering Information Technology, Shandong University of Science and Technology, Jinan 250031, China; lianjianlian  
based on different perspectives [8; 9]. The most commonly used in image segmentation methods are listed roughly as follow: segmentation based on edge detection, region extraction, threshold method, and clustering techniques [10; 11]. There also exists graph cut based method which performs image segmentation by using both edge and regional information [12]. Besides, segmentation may be also viewed as classification problem based on color and spatial features. In this regard, the rough set theory [13], which can extract the discriminative features from original data, has been applied to color image segmentation [14].  
In particular, the K-means and fuzzy c-means (FCM) clustering are two of the most effective and popular algorithms in this field, which are carried out by classifying elements into different regions based on element similarity [15; 16; 17]. K-means clustering is a widely used technique with simple implementation and good convergence speed. This method aims to group pixels of a picture into K clusters, according to the similarity between a pair of data components [17]. In the segmentation process, K clustering centers are first determined, then this method intends to place them as far as possible away from each other for optimally clustering [1]. However, the clustering performance of this method depends heavily on prior knowledge, which may fail when data points described in the feature space are nonspherical clusters. Fuzzy clustering algorithm has been applied to many domains. This method superior to most hard clustering techniques in preserving original image information in the clustering process [18]. FCM is an unsupervised algorithm based on the idea that clustering the data points by minimizing the cost function iteratively and maximizing the distance between cluster centers [19; 20; 21]. However, the FCM algorithm is very sensitive to additive noise due to the lack of consideration of the image context, which lacks algorithm's robustness to deal with image noise [22]. In addition, this method requires a large amount of calculation and often appears an over-segmentation phenomenon.  
[PERSON] et al. [15] propose a new clustering algorithm. It first defines two variables for each data point based on the nature of clustering centers. Then, a decision graph is designed to make the cluster centers isolated from other data points for clustering. However, the strategy in [PERSON] et al. [15] still has drawbacks. For instance, the accuracy of the algorithm largely depends on the choice of threshold which is difficult to define effectively [23]. Besides, the cluster centers must be manually selected from the final decision graph generated by the algorithm.  
In this paper, 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. More specifically, we define a function to separate the clustering centers and other data points, change the way to calculate variables. In addition, this algorithm requires neither iteration nor prior knowledge, which can simply select the cluster centers and return the number of clusters.  
The rest of this paper is organized as follows. We first describe the architecture of our work in Section 2. Experimental results are demonstrated in Section 3. Section 4 presents the conclusion and future work in image segmentation.