智能系统学报2017，Vol.12Issue(2)：179-187,9.DOI:10.11992/tis.201603005
知识迁移的极大熵聚类算法及其在纹理图像分割中的应用
A maximum entropy clustering algorithm based on knowledge transfer and its application to texture image segmentation
摘要
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.关键词
迁移学习/中心迁移匹配/极大熵聚类/纹理图像分割/抗噪性Key words
transfer learning/center transfer matching/maximum entropy clustering/texture image segmentation/robustness分类
信息技术与安全科学引用本文复制引用
程旸,蒋亦樟,钱鹏江,王士同..知识迁移的极大熵聚类算法及其在纹理图像分割中的应用[J].智能系统学报,2017,12(2):179-187,9.基金项目
国家自然科学基金项目(61572236) （61572236）
江苏省自然科学基金项目(BK20160187) （BK20160187）
江苏省产学研前瞻性联合研究项目(BY2013015-02). （BY2013015-02）
