维普中文期刊产品整合服务

Unsupervised Color Segmentation with Reconstructed Spatial Weighted Gaussian Mixture Model and Random Color Histogram

查看全文 作　　者：Umer Sadiq [1,2]Khan;Zhen [1,2]Liu;Fang [1,2]Xu;Muhib Ullah [3,4]Khan;Lerui [5]Chen;Touseef Ahmed [4,6]Khan;Muhammad Kashif [7]Khattak;Yuquan [8]Zhang 高影响力作者 机构地区：[1]School of Computer and Information Science,Hubei Engineering University,Xiaogan,432000,China;[2]Institute for AI Industrial Technology Research,Hubei Engineering University,Xiaogan,432000,China;[3]Key Lab of Mountain Hazards and Surface Processes,Institute of Mountain Hazards and Environment,Chinese Academy of Sciences,Chengdu,610041,China;[4]University of Chinese Academy of Sciences,Beijing,100049,China;[5]College of Aviation,Zhongyuan University of Technology,Zhengzhou,451191,China;[6]Research Center of Digital Mountain and Remote Sensing Application,Institute of Mountain Hazards and Environment,Chinese Academy of Sciences,Chengdu,610041,China;[7]School of Computer Science,University of Poonch,Azad Jammu Kashmir,12350,Pakistan;[8]School of Computer Science and Information Engineering,Hubei University,Wuhan,430062,China高影响力机构 出　　处：《Computers, Materials & Continua》索引2024年第78卷第3期,共26页高影响力期刊 基　　金：supported by the MOE(Ministry of Education of China)Project of Humanities and Social Sciences(23YJAZH169);the Hubei Provincial Department of Education Outstanding Youth Scientific Innovation Team Support Foundation(T2020017);Henan Foreign Experts Project No.HNGD2023027. 摘　　要：Image classification and unsupervised image segmentation can be achieved using the Gaussian mixture model.Although the Gaussian mixture model enhances the flexibility of image segmentation,it does not reflect spatial information and is sensitive to the segmentation parameter.In this study,we first present an efficient algorithm that incorporates spatial information into the Gaussian mixture model(GMM)without parameter estimation.The proposed model highlights the residual region with considerable information and constructs color saliency.Second,we incorporate the content-based color saliency as spatial information in the Gaussian mixture model.The segmentation is performed by clustering each pixel into an appropriate component according to the expectation maximization and maximum criteria.Finally,the random color histogram assigns a unique color to each cluster and creates an attractive color by default for segmentation.A random color histogram serves as an effective tool for data visualization and is instrumental in the creation of generative art,facilitating both analytical and aesthetic objectives.For experiments,we have used the Berkeley segmentation dataset BSDS-500 and Microsoft Research in Cambridge dataset.In the study,the proposed model showcases notable advancements in unsupervised image segmentation,with probabilistic rand index(PRI)values reaching 0.80,BDE scores as low as 12.25 and 12.02,compactness variations at 0.59 and 0.7,and variation of information(VI)reduced to 2.0 and 1.49 for the BSDS-500 and MSRC datasets,respectively,outperforming current leading-edge methods and yielding more precise segmentations. 关 键 词：Unsupervised segmentation color saliency spatial weighted GMM random color histogram
相关文献

参考文献(93)

耦合文献(14)

网站首页 | 关于我们 | 联系我们 | 产品服务 | 客服中心 | 广告服务 | 版权声明 | 网站联盟 | 友情链接 | 售卡网点

版权所有© 渝B2-20050021-1　渝公网安备 50019002500403号　违法和不良信息举报中心

互联网出版许可证 新出网证(渝)字10号 全国400电话 - 免长途话费