Maoguo Gong
Xidian University
506 Papers
1.4K Citations
Maoguo Gong is an academic researcher from Xidian University. The author has contributed to research in topics: Computer science & Evolutionary algorithm. The author has an hindex of 56, co-authored 377 publications. Previous affiliations of Maoguo Gong include Shandong University of Science and Technology & Chinese Ministry of Education.
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Papers
Three-Class Change Detection in Synthetic Aperture Radar Images Based on Deep Belief Network
Qiunan Zhao,Maoguo Gong,Hao Li,Tao Zhan,Qian Wang +4 more
- 25 Sep 2015
TL;DR: A novel three-class change detection approach for synthetic aperture radar images (SAR) based on deep learning to recognize the positive changed pixels, negative changed pixels and unchanged pixels.
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Multiobjective multitasking optimization assisted by multidirectional prediction method
TL;DR: Wang et al. as mentioned in this paper designed an efficient transfer strategy based on multidirectional prediction method, where the population is divided into multiple classes by the binary clustering method, and the representative point of each class is calculated.
Deep Fuzzy Variable C-Means Clustering Incorporated with Curriculum Learning
TL;DR: Deep Fuzzy Curriculum Clustering (DFC) as discussed by the authors is a deep clustering method that utilizes deep neural networks to jointly learn representation features and clustering assignments.
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A multiobjective optimization method based on MOEA/D and fuzzy clustering for change detection in SAR images
Wang Qiao,Hao Li,Maoguo Gong,Linzhi Su,Licheng Jiao +4 more
- 06 Jul 2014
TL;DR: An innovation for change detection in synthetic aperture radar images is put forward that integrates evolutionary computation into fuzzy clustering process, and considers detail preserving capability and noise removing capability as two separate objectives for multiobjective optimization, and thus transforming the change detection problem into a multiobjectives optimization problem (MOP).
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Interval Type-2 Fuzzy Logic for Semisupervised Multimodal Hashing
TL;DR: The proposed label estimation method has been experimentally proven to be more feasible for a multilabeled MIRFlickr data set in a hash lookup task and can still compete with the worst compared supervised one.
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