His primary scientific interests are in Artificial intelligence, Computer vision, Pattern recognition, Feature extraction and Machine learning. While working in this field, Hongxun Yao studies both Artificial intelligence and Affect. His Pattern recognition research is multidisciplinary, incorporating perspectives in Recurrent neural network, Image, Visual Word and Background subtraction.
The Feature extraction study combines topics in areas such as Dimension, Solid modeling, Histogram, RGB color model and Kernel. His research integrates issues of Active appearance model, Sparse approximation, Neural coding, Convolutional neural network and Robustness in his study of Eye tracking. His work deals with themes such as Landmark and Mobile device, which intersect with Discriminative model.
His main research concerns Artificial intelligence, Pattern recognition, Computer vision, Feature extraction and Machine learning. Image, Discriminative model, Feature, Image retrieval and Video tracking are the primary areas of interest in his Artificial intelligence study. His Image retrieval research is multidisciplinary, relying on both Information retrieval and Support vector machine.
His Pattern recognition research integrates issues from Contextual image classification, Histogram, Object and Representation. His Computer vision study often links to related topics such as Robustness. In most of his Feature extraction studies, his work intersects topics such as Visualization.
Artificial intelligence, Pattern recognition, Feature, Object and Image are his primary areas of study. His study in Artificial intelligence is interdisciplinary in nature, drawing from both Machine learning and Computer vision. His Computer vision study combines topics from a wide range of disciplines, such as Computational complexity theory and Frame.
His Pattern recognition research incorporates themes from 3D reconstruction and Recurrent neural network. His Feature study combines topics in areas such as Deep learning and Pattern recognition. His research in Image intersects with topics in Range and Visualization.
Hongxun Yao mainly investigates Artificial intelligence, Pattern recognition, Feature, Computer vision and Convolutional neural network. He incorporates Artificial intelligence and Action recognition in his studies. The study incorporates disciplines such as Recurrent neural network, Quantization and Hash function in addition to Pattern recognition.
In general Computer vision study, his work on Texture and Artifact often relates to the realm of Noise, thereby connecting several areas of interest. His Convolutional neural network research is multidisciplinary, relying on both Image processing, Artificial neural network and Eye tracking. The Feature extraction study combines topics in areas such as Machine learning and Robustness.
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Hedged Deep Tracking
Yuankai Qi;Shengping Zhang;Lei Qin;Hongxun Yao.
computer vision and pattern recognition (2016)
Auto-encoder based dimensionality reduction
Yasi Wang;Hongxun Yao;Sicheng Zhao.
Deep Feature Fusion for VHR Remote Sensing Scene Classification
Souleyman Chaib;Huan Liu;Yanfeng Gu;Hongxun Yao.
IEEE Transactions on Geoscience and Remote Sensing (2017)
Sparse coding based visual tracking: Review and experimental comparison
Shengping Zhang;Hongxun Yao;Xin Sun;Xiusheng Lu.
Pattern Recognition (2013)
Exploring Principles-of-Art Features For Image Emotion Recognition
Sicheng Zhao;Yue Gao;Xiaolei Jiang;Hongxun Yao.
acm multimedia (2014)
Location Discriminative Vocabulary Coding for Mobile Landmark Search
Rongrong Ji;Ling-Yu Duan;Jie Chen;Hongxun Yao.
International Journal of Computer Vision (2012)
Continuous Probability Distribution Prediction of Image Emotions via Multitask Shared Sparse Regression
Sicheng Zhao;Hongxun Yao;Yue Gao;Rongrong Ji.
IEEE Transactions on Multimedia (2017)
Robust visual tracking based on online learning sparse representation
Shengping Zhang;Hongxun Yao;Huiyu Zhou;Xin Sun.
Predicting Personalized Image Emotion Perceptions in Social Networks
Sicheng Zhao;Hongxun Yao;Yue Gao;Guiguang Ding.
IEEE Transactions on Affective Computing (2018)
An image fragile watermark scheme based on chaotic image pattern and pixel-pairs
Shao-Hui Liu;Hong-Xun Yao;Wen Gao;Yong-Liang Liu.
Applied Mathematics and Computation (2007)
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