S
Shan Zeng
Researcher at Wuhan Polytechnic University
Publications - 25
Citations - 425
Shan Zeng is an academic researcher from Wuhan Polytechnic University. The author has contributed to research in topics: Hyperspectral imaging & Cluster analysis. The author has an hindex of 9, co-authored 25 publications receiving 256 citations. Previous affiliations of Shan Zeng include Huazhong University of Science and Technology.
Papers
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Image retrieval using spatiograms of colors quantized by Gaussian Mixture Models
TL;DR: A novel image representation method that characterizes an image as a spatiogram--a generalized histogram--of colors quantized by Gaussian Mixture Models (GMMs) is proposed, which employs Gaussian color components instead of discrete color bins.
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A Unified Collaborative Multikernel Fuzzy Clustering for Multiview Data
TL;DR: A clustering model that unifies the local partitions and global clustering in a collaborative learning framework and demonstrates that the proposed algorithm outperformed the related state-of-the-art algorithms in comparison, which included multitask, multikernel, and multiview clustering approaches.
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Image segmentation using spectral clustering of Gaussian mixture models
TL;DR: A novel image segmentation method that combines spectral clustering and Gaussian mixture models is presented in this paper and the experimental evaluation on the IRIS dataset and the real-world image segmentsation problem demonstrates the effectiveness of the proposed approach.
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Graph weeds net: A graph-based deep learning method for weed recognition
TL;DR: A novel graph-based deep learning architecture, namely Graph Weeds Net (GWN), which aims to recognize multiple types of weeds from conventional RGB images collected from complex rangelands, and provides suggestions for key regions, creating opportunities for further within-image actions for robotic in-field systems.
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A study on multi-kernel intuitionistic fuzzy C-means clustering with multiple attributes
TL;DR: This paper proposes to address the issue of IFCM with multi-kernel mapping, where different types of features are grouped and a composite kernel is constructed to map each attribute group into an individual kernel space and to linearly combine these kernels with optimimal weights.