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 continge...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.展开更多
Many data mining applications have a large amount of data but labeling data is usually difficult, expensive, or time consuming, as it requires human experts for annotation. Semi-supervised learning addresses this prob...Many data mining applications have a large amount of data but labeling data is usually difficult, expensive, or time consuming, as it requires human experts for annotation. Semi-supervised learning addresses this problem by using unlabeled data together with labeled data in the training process. Co-Training is a popular semi-supervised learning algorithm that has the assumptions that each example is represented by multiple sets of features （views） and these views are sufficient for learning and independent given the class. However, these assumptions axe strong and are not satisfied in many real-world domains. In this paper, a single-view variant of Co-Training, called Co-Training by Committee （CoBC） is proposed, in which an ensemble of diverse classifiers is used instead of redundant and independent views. We introduce a new labeling confidence measure for unlabeled examples based on estimating the local accuracy of the committee members on its neighborhood. Then we introduce two new learning algorithms, QBC-then-CoBC and QBC-with-CoBC, which combine the merits of committee-based semi-supervised learning and active learning. The random subspace method is applied on both C4.5 decision trees and 1-nearest neighbor classifiers to construct the diverse ensembles used for semi-supervised learning and active learning. Experiments show that these two combinations can outperform other non committee-based ones.展开更多
基金the Ethiopian Ministry of Education(MoE)the Deutscher Akademischer Auslandsdienst(DAAD)for funding this research work(funding number 57162925).
文摘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partially supported by the Transregional Collaborative Research Centre SFB/TRR 62 Companion-Technology for Cognitive Technical Systems funded by the German Research Foundation(DFG)supported by a scholarship of the German Academic Exchange Service(DAAD)
文摘Many data mining applications have a large amount of data but labeling data is usually difficult, expensive, or time consuming, as it requires human experts for annotation. Semi-supervised learning addresses this problem by using unlabeled data together with labeled data in the training process. Co-Training is a popular semi-supervised learning algorithm that has the assumptions that each example is represented by multiple sets of features （views） and these views are sufficient for learning and independent given the class. However, these assumptions axe strong and are not satisfied in many real-world domains. In this paper, a single-view variant of Co-Training, called Co-Training by Committee （CoBC） is proposed, in which an ensemble of diverse classifiers is used instead of redundant and independent views. We introduce a new labeling confidence measure for unlabeled examples based on estimating the local accuracy of the committee members on its neighborhood. Then we introduce two new learning algorithms, QBC-then-CoBC and QBC-with-CoBC, which combine the merits of committee-based semi-supervised learning and active learning. The random subspace method is applied on both C4.5 decision trees and 1-nearest neighbor classifiers to construct the diverse ensembles used for semi-supervised learning and active learning. Experiments show that these two combinations can outperform other non committee-based ones.
