Title | ||
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Knowledge-leveraged transfer fuzzy C -Means for texture image segmentation with self-adaptive cluster prototype matching. |
Abstract | ||
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We study a novel fuzzy clustering method to improve the segmentation performance on the target texture image by leveraging the knowledge from a prior texture image. Two knowledge transfer mechanisms, i.e. knowledge-leveraged prototype transfer (KL-PT) and knowledge-leveraged prototype matching (KL-PM) are first introduced as the bases. Applying them, the knowledge-leveraged transfer fuzzy C-means (KL-TFCM) method and its three-stage-interlinked framework, including knowledge extraction, knowledge matching, and knowledge utilization, are developed. There are two specific versions: KL-TFCM-c and KL-TFCM-f, i.e. the so-called crisp and flexible forms, which use the strategies of maximum matching degree and weighted sum, respectively. The significance of our work is fourfold: 1) Owing to the adjustability of referable degree between the source and target domains, KL-PT is capable of appropriately learning the insightful knowledge, i.e. the cluster prototypes, from the source domain; 2) KL-PM is able to self-adaptively determine the reasonable pairwise relationships of cluster prototypes between the source and target domains, even if the numbers of clusters differ in the two domains; 3) The joint action of KL-PM and KL-PT can effectively resolve the data inconsistency and heterogeneity between the source and target domains, e.g. the data distribution diversity and cluster number difference. Thus, using the three-stage-based knowledge transfer, the beneficial knowledge from the source domain can be extensively, self-adaptively leveraged in the target domain. As evidence of this, both KL-TFCM-c and KL-TFCM-f surpass many existing clustering methods in texture image segmentation; and 4) In the case of different cluster numbers between the source and target domains, KL-TFCM-f proves higher clustering effectiveness and segmentation performance than does KL-TFCM-c. |
Year | DOI | Venue |
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2017 | 10.1016/j.knosys.2017.05.018 | Knowledge-Based Systems |
Keywords | Field | DocType |
Fuzzy C-means (FCM),Transfer learning,Knowledge transfer,Image segmentation,Data heterogeneity | Fuzzy clustering,Data mining,Computer science,Knowledge transfer,Image segmentation,Artificial intelligence,Cluster analysis,Pattern recognition,Segmentation,Fuzzy logic,Determining the number of clusters in a data set,Knowledge extraction,Machine learning | Journal |
Volume | ISSN | Citations |
130 | 0950-7051 | 6 |
PageRank | References | Authors |
0.43 | 41 | 7 |
Name | Order | Citations | PageRank |
---|---|---|---|
Pengjiang Qian | 1 | 133 | 11.25 |
Kaifa Zhao | 2 | 19 | 3.65 |
Yizhang Jiang | 3 | 382 | 27.24 |
Kuan-Hao Su | 4 | 24 | 5.46 |
Zhaohong Deng | 5 | 50 | 4.35 |
Shitong Wang | 6 | 1485 | 109.13 |
Raymond F. Muzic Jr. | 7 | 28 | 4.48 |