Superpixel Segmentation Based on Grid Point Density Peak Clustering

被引:3
|
作者
Chen, Xianyi [1 ]
Peng, Xiafu [1 ]
Wang, Sun'an [2 ]
机构
[1] Xiamen Univ, Dept Automat, Xiamen 361005, Peoples R China
[2] Xi An Jiao Tong Univ, Sch Mech Engn, Xian 710049, Peoples R China
关键词
superpixel segmentation; density clustering; image preprocessing; computer vision;
D O I
10.3390/s21196374
中图分类号
O65 [分析化学];
学科分类号
070302 ; 081704 ;
摘要
Superpixel segmentation is one of the key image preprocessing steps in object recognition and detection methods. However, the over-segmentation in the smoothly connected homogenous region in an image is the key problem. That would produce redundant complex jagged textures. In this paper, the density peak clustering will be used to reduce the redundant superpixels and highlight the primary textures and contours of the salient objects. Firstly, the grid pixels are extracted as feature points, and the density of each feature point will be defined. Secondly, the cluster centers are extracted with the density peaks. Finally, all the feature points will be clustered by the density peaks. The pixel blocks, which are obtained by the above steps, are superpixels. The method is carried out in the BSDS500 dataset, and the experimental results show that the Boundary Recall (BR) and Achievement Segmentation Accuracy (ASA) are 95.0% and 96.3%, respectively. In addition, the proposed method has better performance in efficiency (30 fps). The comparison experiments show that not only do the superpixel boundaries have good adhesion to the primary textures and contours of the salient objects, but they can also effectively reduce the redundant superpixels in the homogeneous region.
引用
收藏
页数:15
相关论文
共 50 条
  • [21] An image segmentation fusion algorithm based on density peak clustering and Markov random field
    Feng Y.
    Liu W.
    Zhang X.
    Zhu X.
    Multimedia Tools and Applications, 2024, 83 (37) : 85331 - 85355
  • [22] Weakly Supervised Semantic Segmentation Based on Superpixel Sampling Clustering Networks
    Xiao, Jun-sheng
    Xu, Hua-hu
    Ma, Xiao-jin
    PROCEEDINGS OF THE 2ND INTERNATIONAL CONFERENCE ON COMPUTER SCIENCE AND SOFTWARE ENGINEERING (CSSE 2019), 2019,
  • [23] Weak boundary preserved superpixel segmentation based on directed graph clustering
    Xu, Li
    Luo, Bing
    Pei, Zheng
    SIGNAL PROCESSING-IMAGE COMMUNICATION, 2018, 65 : 231 - 239
  • [24] Fast Superpixel-Based Clustering Algorithm for SAR Image Segmentation
    Jing, Wenbo
    Jin, Tian
    Xiang, Deliang
    IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, 2022, 19
  • [25] Unsupervised Superpixel-Based Segmentation of Histopathological Images with Consensus Clustering
    Fouad, Shereen
    Randell, David
    Galton, Antony
    Mehanna, Hisham
    Landini, Gabriel
    MEDICAL IMAGE UNDERSTANDING AND ANALYSIS (MIUA 2017), 2017, 723 : 767 - 779
  • [26] A New Multistage Medical Segmentation Method Based on Superpixel and Fuzzy Clustering
    Ji, Shiyong
    Wei, Benzheng
    Yu, Zhen
    Yang, Gongping
    Yin, Yilong
    COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE, 2014, 2014
  • [27] Superpixel segmentation of PolSAR images based on improved local iterative clustering
    Han, Binbin
    Han, Ping
    Cheng, Zheng
    INTERNATIONAL JOURNAL OF REMOTE SENSING, 2022, 43 (08) : 2735 - 2754
  • [28] Superpixel Segmentation using Linear Spectral Clustering
    Li, Zhengqin
    Chen, Jiansheng
    2015 IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR), 2015, : 1356 - 1363
  • [29] Density estimation method of mature wheat based on point cloud segmentation and clustering
    Zou, Rong
    Zhang, Yu
    Chen, Jin
    Li, Jinyan
    Dai, Wenjie
    Mu, Senlin
    COMPUTERS AND ELECTRONICS IN AGRICULTURE, 2023, 205
  • [30] Superpixel Segmentation via Density Peaks
    Shah, Sayed Asad Hussain
    Li, Liang
    Li, Yajun
    Zhang, Jiawan
    2021 4TH INTERNATIONAL CONFERENCE ON INFORMATION AND COMPUTER TECHNOLOGIES (ICICT 2021), 2021, : 93 - 98