A Grouping-Feature and Nesting-Kernel Scene Image Segmentation Algorithm

摘要：

　　framework of Grouping-Feature and Nesting-Kernel Support Vector Machine(GFNK-SVM)methodology is proposed to achieve a more reliable and robust segmentation performance.Firstly,the pixel wise intensity,gradient and SMF features are extracted to provide multiple features of the samples of GFNK-SVM model.A new clustering method called as Clustering Validity-Interval Type-2 Fuzzy C-Means(CV-IT2FCM)clustering algorithm is also presented to improve the robustness and reliability of clustering results by the iterative optimization.A type-2 fuzzy criterion is integrated to handle uncertainties in the clustering optimization process and CV is employed to select the training samples for the learning of the novel SVM model.Finally,by integrating SVM with a novel Nesting-Kernel,a systematic GFNK-SVM framework is presented and its model is trained as classifier for scene images segmentation.The GFNK-SVM scene segmentation method combined the advantages of multiple features and multiple kernels.Extensive experimental results on the BSDS dataset demonstrate that our method can obtain better performances than those state-of-the-art segmentation techniques.

关键词： Nesting-kernel Grouping-Feature Support Vector Machine Interval type-2 Fuzzy Criterion

作者: XU Shuqiong Zhu Cailian Yuan Conggui

作者单位: Department of Electronic Engineering,Dongguan Polytechnic,Dongguan Guangdong 523808,China;Faculty of Department of Electronic Engineering,Dongguan Polytechnic,Dongguan Guangdong 523808,China

会议类型: 国际会议

会议名称: The 33th Chinese Control Conference第33届中国控制会议

会议地点: 南京

会议语种:英文

页码: 4747-4752

在线出版日期: 2014-07-28（万方平台首次上网日期，不代表论文的发表时间）