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Yiu-ming Cheung
Researcher at Hong Kong Baptist University
Publications - 336
Citations - 6697
Yiu-ming Cheung is an academic researcher from Hong Kong Baptist University. The author has contributed to research in topics: Cluster analysis & Computer science. The author has an hindex of 37, co-authored 300 publications receiving 5294 citations. Previous affiliations of Yiu-ming Cheung include The Chinese University of Hong Kong & Huazhong University of Science and Technology.
Papers
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Segmentation of retinal blood vessels using the radial projection and semi-supervised approach
TL;DR: A novel scheme of extracting the retinal vessels based on the radial projection and semi-supervised method that can achieve improved detection of thin vessels and decrease false detection of vessels in pathological regions compared to rival solutions is presented.
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Agglomerative Fuzzy K-Means Clustering Algorithm with Selection of Number of Clusters
TL;DR: An agglomerative fuzzy K-means clustering algorithm for numerical data is presented, an extension to the standard fuzzy K -means algorithm by introducing a penalty term to the objective function to make the clustering process not sensitive to the initial cluster centers.
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Robust Object Tracking via Key Patch Sparse Representation
TL;DR: Comparing the KPSR with eight other contemporary tracking methods on 13 benchmark video data sets, the experimental results show that the K PSR tracker outperforms classical or state-of-the-art tracking methods in the presence of partial occlusion, background clutter, and illumination change.
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Feature Selection and Kernel Learning for Local Learning-Based Clustering
Hong Zeng,Yiu-ming Cheung +1 more
TL;DR: The aim of this paper is to obtain an appropriate data representation through feature selection or kernel learning within the framework of the Local Learning-Based Clustering (LLC) method, which can outperform the global learning-based ones when dealing with the high-dimensional data lying on manifold.
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k -means: a new generalized k -means clustering algorithm
TL;DR: In this article, a generalized version of the conventional k-means clustering algorithm is presented, which is applicable to ellipse-shaped data clusters without dead-unit problem, and performs correct clustering without pre-assigning the exact cluster number.