[1]CHENG Yang,JIANG Yizhang,QIAN Pengjiang,et al.A maximum entropy clustering algorithm based on knowledge transfer and its application to texture image segmentation[J].CAAI Transactions on Intelligent Systems,2017,12(2):179-187.[doi:10.11992/tis.201603005]
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A maximum entropy clustering algorithm based on knowledge transfer and its application to texture image segmentation
CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume:
12
Number of periods:
2017 2
Page number:
179-187
Column:
学术论文—机器感知与模式识别
Public date:
2017-05-05
- Title:
- A maximum entropy clustering algorithm based on knowledge transfer and its application to texture image segmentation
- Keywords:
- transfer learning; center transfer matching; maximum entropy clustering; texture image segmentation; robustness
- CLC:
- TP181
- DOI:
- 10.11992/tis.201603005
- Abstract:
- In this paper, we propose a novel technique for maximum entropy clustering (MEC) based on knowledge transfer. More specifically, we aim to solve the following two challenging questions. First, how can knowledge be appropriately selected from a source domain to enhance clustering performance in the target domain via transfer learning? Second, how best do we conduct transfer clustering if the number of clusters in the source domain and the target domain are inconsistent? To address these questions, we designed a new transfer clustering mechanism called the central matching transfer mechanism, which we based on clustering centers. Further, we developed a knowledge-transfer-based maximum entropy clustering (KT-MEC) algorithm by incorporating our mechanism into the classic MEC approach. Our experimental results reveal that our proposed KT-MEC algorithm achieves a higher level of accuracy and better noise immunity than many existing methods when applied to texture image segmentation in different transfer scenarios.
- References:
-
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