Artificial intelligence, Pattern recognition, Dimensionality reduction, Computer vision and Convolutional neural network are his primary areas of study. His work on Artificial intelligence is being expanded to include thematically relevant topics such as Machine learning. Shiming Xiang has included themes like Contextual image classification, Pixel, Regression analysis and Cluster analysis in his Pattern recognition study.
His Dimensionality reduction research includes elements of Linear discriminant analysis, Algorithm, Structure tensor, Principal component analysis and Calculus. His Convolutional neural network study combines topics from a wide range of disciplines, such as Segmentation, End-to-end principle, Deep learning, Residual and Range. His Feature extraction study deals with Artificial neural network intersecting with Iterative method.
Shiming Xiang mostly deals with Artificial intelligence, Pattern recognition, Computer vision, Image and Feature extraction. His study in Segmentation, Image segmentation, Convolutional neural network, Contextual image classification and Feature falls under the purview of Artificial intelligence. While the research belongs to areas of Convolutional neural network, Shiming Xiang spends his time largely on the problem of Theoretical computer science, intersecting his research to questions surrounding Point.
His research in Pattern recognition intersects with topics in Pixel and Cluster analysis. The concepts of his Image study are interwoven with issues in Machine learning and Representation. His Linear discriminant analysis study combines topics in areas such as Curse of dimensionality and Dimensionality reduction.
His primary areas of study are Artificial intelligence, Pattern recognition, Computer vision, Convolutional neural network and Feature extraction. All of his Artificial intelligence and Image, Object detection, Segmentation, Feature and Deep learning investigations are sub-components of the entire Artificial intelligence study. His studies in Feature integrate themes in fields like Detector and Closed captioning.
His Pattern recognition study incorporates themes from Contextual image classification and Cluster analysis. He focuses mostly in the field of Computer vision, narrowing it down to matters related to Focus and, in some cases, Semantics. His Convolutional neural network study also includes
His primary scientific interests are in Artificial intelligence, Convolutional neural network, Pattern recognition, Feature extraction and Theoretical computer science. Shiming Xiang performs multidisciplinary study on Artificial intelligence and Architecture in his works. His Convolutional neural network research incorporates themes from Neural coding, Function approximation and Cluster analysis.
Shiming Xiang combines subjects such as Contextual image classification and Iterative reconstruction with his study of Pattern recognition. His Feature extraction research includes themes of Machine learning, Prior probability, Range, RGB color model and Focus. His Theoretical computer science research incorporates elements of Point and Representation.
This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.
Efficient Image Dehazing with Boundary Constraint and Contextual Regularization
Gaofeng Meng;Ying Wang;Jiangyong Duan;Shiming Xiang.
international conference on computer vision (2013)
Learning a Mahalanobis distance metric for data clustering and classification
Shiming Xiang;Feiping Nie;Changshui Zhang.
Pattern Recognition (2008)
Vehicle Detection in Satellite Images by Hybrid Deep Convolutional Neural Networks
Xueyun Chen;Shiming Xiang;Cheng-Lin Liu;Chun-Hong Pan.
IEEE Geoscience and Remote Sensing Letters (2014)
Face detection based on multi-block LBP representation
Lun Zhang;Rufeng Chu;Shiming Xiang;Shengcai Liao.
international conference on biometrics (2007)
Trace ratio criterion for feature selection
Feiping Nie;Shiming Xiang;Yangqing Jia;Changshui Zhang.
national conference on artificial intelligence (2008)
Discriminative Least Squares Regression for Multiclass Classification and Feature Selection
Shiming Xiang;Feiping Nie;Gaofeng Meng;Chunhong Pan.
IEEE Transactions on Neural Networks (2012)
Relation-Shape Convolutional Neural Network for Point Cloud Analysis
Yongcheng Liu;Bin Fan;Shiming Xiang;Chunhong Pan.
computer vision and pattern recognition (2019)
Automatic Road Detection and Centerline Extraction via Cascaded End-to-End Convolutional Neural Network
Guangliang Cheng;Ying Wang;Shibiao Xu;Hongzhen Wang.
IEEE Transactions on Geoscience and Remote Sensing (2017)
Deep Adaptive Image Clustering
Jianlong Chang;Lingfeng Wang;Gaofeng Meng;Shiming Xiang.
international conference on computer vision (2017)
Learning Consistent Feature Representation for Cross-Modal Multimedia Retrieval
Cuicui Kang;Shiming Xiang;Shengcai Liao;Changsheng Xu.
IEEE Transactions on Multimedia (2015)
If you think any of the details on this page are incorrect, let us know.
We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below: