摘要
Segmentation is the act of partitioning an image into different regions by creating boundaries between regions.k-means image segmentation is the simplest prevalent approach.However,the segmentation quality is contingent on the initial parameters(the cluster centers and their number).In this paper,a convolution-based modified adaptive k-means(MAKM)approach is proposed and evaluated using images collected from different sources(MATLAB,Berkeley image database,VOC2012,BGH,MIAS,and MRI).The evaluation shows that the proposed algorithm is superior to k-means++,fuzzy c-means,histogrambased k-means,and subtractive k-means algorithms in terms of image segmentation quality(Q-value),computational cost,and RMSE.The proposed algorithm was also compared to state-of-the-art learning-based methods in terms of IoU and MIoU;it achieved a higher MIoU value.
Segmentation is the act of partitioning an image into different regions by creating boundaries between regions. k-means image segmentation is the simplest prevalent approach. However, the segmentation quality is contingent on the initial parameters(the cluster centers and their number). In this paper, a convolution-based modified adaptive k-means(MAKM)approach is proposed and evaluated using images collected from different sources(MATLAB, Berkeley image database, VOC2012, BGH, MIAS, and MRI).The evaluation shows that the proposed algorithm is superior to k-means++, fuzzy c-means, histogrambased k-means, and subtractive k-means algorithms in terms of image segmentation quality(Q-value),computational cost, and RMSE. The proposed algorithm was also compared to state-of-the-art learning-based methods in terms of IoU and MIoU; it achieved a higher MIoU value.
基金
the Ethiopian Ministry of Education(MoE)
the Deutscher Akademischer Auslandsdienst(DAAD)for funding this research work(funding number 57162925).
作者简介
Taye Girma Debelee,is a sandwich program Ph.D.student in Ulm University and Addis Ababa Science and Technology University.He holds an M.Sc.degree in computer engineering from Addis Ababa University,Ethiopia.His research interests are in digital imaging processing,pattern recognition,data mining and deep learning.E-mail:T.G.Debelee,taye.debelee@uni-ulm.de;Friedhelm Schwenker is a senior lecturer and researcher at the Institute of Neural Information Processing,Ulm University,Germany.His research interests are in artificial neural networks,pattern recognition,data mining,and affective computing.E-mail:F.Schwenker,friedhelm.schwenker@uni-ulm.de;Samuel Rahimeto is an M.Sc.student at Addis Ababa Science and Technology University,Ethiopia.His research interests are in digital image processing and machine learning.E-mail:S.Rahimeto,samuelrahimeto@gmail.com;Dereje Yohannes is a director of the Artificial Intelligence Excellence Center.He holds M.Sc.and Ph.D.degrees in computer engineering.His main research interests are in network security and wireless.He is also working in the area of data mining and machine learning.E-mail:D.Yohannes,derejey@yahoo.com.
