J
Jeng-Shyang Pan
Researcher at Shandong University of Science and Technology
Publications - 889
Citations - 14887
Jeng-Shyang Pan is an academic researcher from Shandong University of Science and Technology. The author has contributed to research in topics: Digital watermarking & Computer science. The author has an hindex of 50, co-authored 789 publications receiving 11645 citations. Previous affiliations of Jeng-Shyang Pan include National Kaohsiung Normal University & Technical University of Ostrava.
Papers
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Book ChapterDOI
An Efficient K -Medoids-Based Algorithm Using Previous Medoid Index, Triangular Inequality Elimination Criteria, and Partial Distance Search
TL;DR: A novel and efficient approach is proposed to reduce the computational complexity of such k-medoids-based algorithms by using previous medoid index, triangular inequality elimination criteria and partial distance search.
Journal Article
Adaptive data-dependent matrix norm based gaussian kernel for facial feature extraction
TL;DR: A novel matrix norm based Gaussian kernel is created which views images as matrices for facial feature extraction, which is the basic kernel for the data-dependent kernel.
Journal ArticleDOI
Face recognition based on fusion of multi-resolution Gabor features
TL;DR: This paper proposes a Gabor-based face recognition method that fuses multi-resolution Gabor features of face images at the matching score level and illustrates that in face recognition, the low-resolution representation of the phase of the Gabor feature such as the code of thephase is more discriminative than the phase itself.
Journal Article
Hyperspectral Image Recognition Using SVM Combined Deep Learning
TL;DR: The kernel learning method termed Support Vector Machine (SVM) applied on feature vectors supplied by deep learning upon hyperspectral image is presented and the learning system is improved by adjusting the parameters and kernel functions to the data structure for improving performance on solving complex tasks.
Journal ArticleDOI
Pairwise IPVO-based reversible data hiding
TL;DR: A novel method based on improved pixel-value-ordering (PVO) and prediction-error expansion is proposed by extending Peng et al.