The scientist’s investigation covers issues in Artificial intelligence, Pattern recognition, Computer vision, Feature extraction and Electroencephalography. His research on Artificial intelligence frequently connects to adjacent areas such as Multilinear map. His studies deal with areas such as Matrix decomposition, Theoretical computer science and Generative model as well as Pattern recognition.
Object detection, Human visual system model, Image, Kadir–Brady saliency detector and Saliency map are among the areas of Computer vision where the researcher is concentrating his efforts. His research integrates issues of Machine vision, Residual and Salience in his study of Image. His Feature extraction research incorporates themes from Neurophysiology, Central nervous system, Support vector machine, Signal processing and Wavelet transform.
His main research concerns Artificial intelligence, Pattern recognition, Computer vision, Feature extraction and Image. Liqing Zhang has researched Artificial intelligence in several fields, including Machine learning, Speech recognition and Electroencephalography. His research investigates the link between Pattern recognition and topics such as Brain–computer interface that cross with problems in Human–computer interaction.
His research investigates the connection between Computer vision and topics such as Sketch that intersect with issues in Information retrieval. His study in Feature extraction is interdisciplinary in nature, drawing from both Speaker recognition, Representation, Linear subspace and Feature. His work in the fields of Composite image filter overlaps with other areas such as Domain.
His primary areas of study are Artificial intelligence, Image, Pattern recognition, Machine learning and Computer vision. His Image study combines topics in areas such as Pooling and Harmonization. His Pattern recognition research incorporates elements of Contextual image classification, Static image and Shot.
He has included themes like Recurrent neural network, Feature extraction, Representation and Projection in his Contextual image classification study. His work on Discriminative model as part of general Machine learning research is often related to Memory module and Quality, thus linking different fields of science. His research on Computer vision often connects related areas such as Sequence.
Liqing Zhang mostly deals with Artificial intelligence, Image, Pattern recognition, Domain and Machine learning. His Deep learning, Embedding and Projection study, which is part of a larger body of work in Artificial intelligence, is frequently linked to Merge and Rate of return on a portfolio, bridging the gap between disciplines. Image is a subfield of Computer vision that he explores.
His work on Inpainting, Enhanced Data Rates for GSM Evolution and Image gradient as part of general Computer vision study is frequently linked to Construct, therefore connecting diverse disciplines of science. He studies Feature vector, a branch of Pattern recognition. His work on Discriminative model is typically connected to Memory module as part of general Machine learning study, connecting several disciplines of science.
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Saliency Detection: A Spectral Residual Approach
Xiaodi Hou;Liqing Zhang.
computer vision and pattern recognition (2007)
Dynamic visual attention: searching for coding length increments
Xiaodi Hou;Liqing Zhang.
neural information processing systems (2008)
Bayesian CP Factorization of Incomplete Tensors with Automatic Rank Determination
Qibin Zhao;Liqing Zhang;Andrzej Cichocki.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2015)
Yang Cao;Changhu Wang;Zhiwei Li;Liqing Zhang.
computer vision and pattern recognition (2010)
Edgel index for large-scale sketch-based image search
Yang Cao;Changhu Wang;Liqing Zhang;Lei Zhang.
computer vision and pattern recognition (2011)
ECG Feature Extraction and Classification Using Wavelet Transform and Support Vector Machines
Qibin Zhao;Liqing Zhang.
international conference on neural networks and brain (2005)
Credit Card Fraud Detection Using Convolutional Neural Networks
Kang Fu;Dawei Cheng;Yi Tu;Liqing Zhang.
international conference on neural information processing (2016)
Bayesian Robust Tensor Factorization for Incomplete Multiway Data
Qibin Zhao;Guoxu Zhou;Liqing Zhang;Andrzej Cichocki.
IEEE Transactions on Neural Networks (2016)
MindFinder: interactive sketch-based image search on millions of images
Yang Cao;Hai Wang;Changhu Wang;Zhiwei Li.
acm multimedia (2010)
Noninvasive BCIs: Multiway Signal-Processing Array Decompositions
A. Cichocki;Y. Washizawa;T. Rutkowski;H. Bakardjian.
IEEE Computer (2008)
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