改进RHGSO-FC算法的RGB-D图像GMM聚类分割

doi:10.3778/j.issn.1002-8331.2308-0318

摘要/Abstract

摘要： 随着低成本深度图像传感器的引入，在RGB-D图像中进行可靠的图像分割是计算机视觉的一个目标，而如何对杂乱的场景进行图像分割具有挑战性。基于随机亨利气体溶解度优化算法的模糊聚类（RHGSO-FC），提出一种新的RGB-D图像分割方法。对亨利气体溶解度优化算法（HGSO）进行改进，提出改进的亨利气体溶解度优化算法（LRHGSO），并利用基于改进亨利气体溶解度优化算法的核模糊聚类（LRHGSO-KFC）生成初始化标签。将初始化标签传入到高斯混合（GMM）聚类中，得到多个聚类结果。最后对这些聚类结果通过聚集超像素方法进行分割合并，得到最终分割结果。实验数据集采用NYU depth V2室内图像，与现有的一些分割方法：阈值分割算法、硬C-均值、模糊C-均值、高斯混合聚类、核模糊聚类、模糊子空间聚类、混沌Kbest引力搜索算法和随机亨利气体溶解度优化算法进行比较，结果表明提出的RGB-D分割算法优于其他比较的算法。

关键词: RGB-D图像分割, 核模糊聚类, 亨利气体溶解度优化算法, 高斯混合模型, 聚集超像素

Abstract: With the introduction of low-cost depth image sensors, reliable image segmentation in RGB-D images is a goal of computer vision, and how to segment images of cluttered scenes is a challenging problem. A new RGB-D image segmentation method based on the random Henry gas solubility optimization-fuzzy clustering (RHGSO-FC) is proposed. Firstly, the Henry gas solubility optimization (HGSO) algorithm is improved to propose the improved Henry gas solubility optimization (LRHGSO) algorithm, and the initialization labels are generated using the the improved Henry gas solubility optimization- kernel fuzzy clustering (LRHGSO-KFC). The initialized labels are then passed into Gaussian mixture model (GMM) clustering to obtain multiple clustering results. Finally these clustering results are segmented and merged by the aggregating superpixels method to get the final segmentation results. The experimental dataset uses NYU depth V2 indoor images and is compared with some of the existing segmentation methods: threshold segmentation algorithm, hard C means, fuzzy C-means, Gaussian mixture model clustering, kernel fuzzy clustering, fuzzy subspace clustering, chaotic Kbest gravitational search algorithm and random Henry gas solubility optimization algorithm. The results show that the RGB-D segmentation algorithm proposed in this paper outperforms the compared algorithms.

Key words: RGB-D image segmentation, kernel fuzzy clustering, Henry gas solubility optimization algorithm, Gaussian mixture model, aggregating superpixels

郭培岩, 范九伦, 刘恒. 改进RHGSO-FC算法的RGB-D图像GMM聚类分割[J]. 计算机工程与应用, 2025, 61(2): 234-246.

GUO Peiyan, FAN Jiulun, LIU Heng. Improved RHGSO-FC Algorithm for GMM Clustering Segmentation of RGB-D Images[J]. Computer Engineering and Applications, 2025, 61(2): 234-246.

参考文献

[1] FU K S, MUI J. A survey on image segmentation[J]. Pattern Recognition, 1981, 13(1): 3-16.
[2] SILBERMAN N, HOIEM D, KOHLI P, et al. Indoor segmentation and support inference from RGBD images[C]//Proceedings of the European Conference on Computer Vision, 2012: 746-760.
[3] IZADI S, KIM D, HILLIGES O, et al. KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera[C]//Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, 2011.
[4] MUTTO C D, ZANUTTIGH P, CORTELAZZO G M. Fusion of geometry and color information for scene segmentation[J]. IEEE Journal of Selected Topics in Signal Processing, 2012, 6(5): 505-521.
[5] HASNAT M A, ALATA O, TRéMEAU A. Unsupervised RGB-D image segmentation using joint clustering and region merging[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012, 6(5): 505-521.
[6] HASNAT M A, ALATA O, TREMEAU A. Joint color-spatial-directional clustering and region merging (JCSD-RM) for unsupervised RGB-D image segmentation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015, 38(11): 2255-2268.
[7] KHAN M R, RAHMAN A M, RAHAMAN G A, et al. Unsupervised RGB-D image segmentation by multi-layer clustering[C]//Proceedings of the 5th International Conference on Informatics, Electronics and Vision, 2016.
[8] MITTAL H, SARASWAT M. cKGSA based fuzzy clustering method for image segmentation of RGB-D images[C]//Proceedings of the 2018 11th International Conference on Contemporary Computing, 2018: 1-6.
[9] YADAV N K, SARASWAT M. A novel fuzzy clustering based method for image segmentation in RGB-D images[J]. Engineering Applications of Artificial Intelligence, 2022, 111: 104709.
[10] CHEN S, ZHANG D. Robust image segmentation using FCM with spatial constraints based on new kernel-induced distance measure[J]. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 2004, 34(4): 1907-1916.
[11] HASHIM F A, HOUSSEIN E H, MABROUK M S, et al. Henry gas solubility optimization: a novel physics-based algorithm[J]. Future Generation Computer Systems, 2019, 101: 646-667.
[12] 李荣雨, 王颖. 基于莱维飞行的改进粒子群算法[J]. 系统仿真学报, 2017, 29(8): 1685-1691.
LI R Y, WANG Y. Improved particle swarm optimization based on Levy flights[J]. Journal of System Simulation, 2017, 29(8): 1685-1691.
[13] KENNEDY J, EBERHART R. Particle swarm optimization[C]//Proceedings of the International Conference on Neural Networks, 1995: 1942-1948.
[14] REYNOLDS D A. Gaussian mixture models[J]. Encyclopedia of Biometrics, 2009, 741: 659-663.
[15] DEMPSTER A P, LAIRD N M, RUBIN D B. Maximum likelihood from incomplete data via the EM algorithm[J]. Journal of the Royal Statistical Society: Series B (methodological), 1977, 39(1): 1-22.
[16] LI Z, WU X M, CHANG S F. Segmentation using superpixels: a bipartite graph partitioning approach[C]//Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition, 2012: 789-796.
[17] REN X, BO L, FOX D. RGB-(D) scene labeling: features and algorithms[C]//Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition, 2012: 2759-2766.
[18] WEI T, WANG X, LI X, et al. Fuzzy subspace clustering noisy image segmentation algorithm with adaptive local variance & non-local information and mean membership linking[J]. Engineering Applications of Artificial Intelligence, 2022, 110: 104672.
[19] WELSTEAD S T. Fractal and wavelet image compression techniques[M]. Bellingham, Washington: Spie Press, 1999.
[20] ECE C, MULLANA M. Image quality assessment techniques pn spatial domain[J]. International Journal of Coal Science & Technology, 2011, 2(3): 177.
[21] WANG Z, BOVIK A C, SHEIKH H R, et al. Image quality assessment: from error visibility to structural similarity[J]. IEEE Transactions on Image Processing, 2004, 13(4): 600-612.
[22] SATHYA B, MANAVALAN R. Image segmentation by clustering methods: performance analysis[J]. International Journal of Computer Applications, 2011, 29(11): 27-32.
[23] FREIXENET J, MUNOZ X, RABA D, et al. Yet another survey on image segmentation: region and boundary information integration[C]//Proceedings of the 7th European Conference on Computer Vision, 2002: 408-422.
[24] UNNIKRISHNAN R, PANTOFARU C, HEBERT M. A measure for objective evaluation of image segmentation algorithms[C]//Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2005: 34.
[25] MARTIN D, FOWLKES C, TAL D, et al. A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics[C]//Proceedings of the 8th IEEE International Conference on Computer Vision, 2001: 416-423.