Dacheng Tao

University of Technology Sydney , Sydney, New South Wales, Australia

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Publications (398)624.6 Total impact

  • [Show abstract] [Hide abstract]
    ABSTRACT: Studies in neuroscience and biological vision have shown that the human retina has strong computational power, and its information representation supports vision tasks on both ventral and dorsal pathways. In this paper, a new local image descriptor, termed Distinctive Efficient Robust Features, or DERF, is derived by modeling the response and distribution properties of the parvocellular-projecting ganglion cells (P-GCs) in the primate retina. DERF features exponential scale distribution, exponential grid structure, and circularly symmetric function Difference of Gaussian (DoG) used as a convolution kernel, all of which are consistent with the characteristics of the ganglion cell array found in neurophysiology, anatomy, and biophysics. In addition, a new explanation for local descriptor design is presented from the perspective of wavelet tight frames. DoG is naturally a wavelet, and the structure of the grid points array in our descriptor is closely related to the spatial sampling of wavelets. The DoG wavelet itself forms a frame, and when we modulate the parameters of our descriptor to make the frame tighter, the performance of the DERF descriptor improves accordingly. This is verified by designing a tight frame DoG (TF-DoG) which leads to much better performance. Extensive experiments conducted in the image matching task on the Multiview Stereo Correspondence Data set demonstrate that DERF outperforms state of the art methods for both hand-crafted and learned descriptors, while remaining robust and being much faster to compute.
  • Lin Zhao, Xinbo Gao, Dacheng Tao, Xuelong Li
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    ABSTRACT: Articulated human pose estimation in unconstrained conditions is a great challenge. We propose a deep structure that represents a human body in different granularity from coarse-to-fine for better detecting parts and describing spatial constrains between different parts. Typical approaches for this problem just utilize a single level structure, which is difficult to capture various body appearances and hard to model high-order part dependencies. In this paper, we build a three layer Markov network to model the body structure that separates the whole body to poselets (combined parts) then to parts representing joints. Parts at different levels are connected through a parent-child relationship to represent high-order spatial relationships. Unlike other multi-layer models, our approach explores more reasonable granularity for part detection and sophisticatedly designs part connections to model body configurations more effectively. Moreover, each part in our model contains different types so as to capture a wide range of pose modes. And our model is a tree structure, which can be trained jointly and favors exact inference. Extensive experimental results on two challenging datasets show the performance of our model improving or being on-par with state-of-the-art approaches.
    Signal Processing 03/2015; 108:36–45. DOI:10.1016/j.sigpro.2014.07.031 · 2.24 Impact Factor
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    Changxing Ding, Dacheng Tao
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    ABSTRACT: The capacity to recognize faces under varied poses is a fundamental human ability that presents a unique challenge for computer vision systems. Compared to frontal face recognition, which has been intensively studied and has gradually matured in the past few decades, pose-invariant face recognition (PIFR) remains a largely unsolved problem. However, PIFR is crucial to realizing the full potential of face recognition for real-world applications, since face recognition is intrinsically a passive biometric technology for recognizing uncooperative subjects. In this paper, we discuss the inherent difficulties in PIFR and present a comprehensive review of established techniques. Existing PIFR methods can be grouped into four categories, i.e., pose-robust feature extraction approaches, multi-view subspace learning approaches, face synthesis approaches, and hybrid approaches. The motivations, strategies, pros/cons, and performance of representative approaches are described and compared. Moreover, promising directions for future research are discussed.
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    Yuan Gao, Miaojing Shi, Dacheng Tao, Chao Xu
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    ABSTRACT: The bag-of-visual-words (BoW) model is effective for representing images and videos in many computer vision problems, and achieves promising performance in image retrieval. Nevertheless, the level of retrieval efficiency in a large-scale database is not acceptable for practical usage. Considering that the relevant images in the database of a given query are more likely to be distinctive than ambiguous, this paper defines “database saliency” as the distinctiveness score calculated for every image to measure its overall “saliency” in the database. By taking advantage of database saliency, we propose a saliency-inspired fast image retrieval scheme, S-sim, which significantly improves efficiency while retains state-of-the-art accuracy in image retrieval. There are two stages in S-sim: the bottom-up saliency mechanism computes the database saliency value of each image by hierarchically decomposing a posterior probability into local patches and visual words, the concurrent information of visual words is then bottom-up propagated to estimate the distinctiveness, and the top-down saliency mechanism discriminatively expands the query via a very low-dimensional linear SVM trained on the top-ranked images after initial search, ranking images are then sorted on their distances to the decision boundary as well as the database saliency values. We comprehensively evaluate S-sim on common retrieval benchmarks, e.g., Oxford and Paris datasets. Thorough experiments suggest that, because of the offline database saliency computation and online low-dimensional SVM, our approach significantly speeds up online retrieval and outperforms the state-of-the-art BoW-based image retrieval schemes.
    IEEE Transactions on Multimedia 02/2015; 17(3):359-369. DOI:10.1109/TMM.2015.2389616 · 1.78 Impact Factor
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    ABSTRACT: Canonical correlation analysis (CCA) has proven an effective tool for two-view dimension reduction due to its profound theoretical foundation and success in practical applications. In respect of multi-view learning, however, it is limited by its capability of only handling data represented by two-view features, while in many real-world applications, the number of views is frequently many more. Although the ad hoc way of simultaneously exploring all possible pairs of features can numerically deal with multi-view data, it ignores the high order statistics (correlation information) which can only be discovered by simultaneously exploring all features. Therefore, in this work, we develop tensor CCA (TCCA) which straightforwardly yet naturally generalizes CCA to handle the data of an arbitrary number of views by analyzing the covariance tensor of the different views. TCCA aims to directly maximize the canonical correlation of multiple (more than two) views. Crucially, we prove that the multi-view canonical correlation maximization problem is equivalent to finding the best rank-1 approximation of the data covariance tensor, which can be solved efficiently using the well-known alternating least squares (ALS) algorithm. As a consequence, the high order correlation information contained in the different views is explored and thus a more reliable common subspace shared by all features can be obtained. In addition, a non-linear extension of TCCA is presented. Experiments on various challenge tasks, including large scale biometric structure prediction, internet advertisement classification and web image annotation, demonstrate the effectiveness of the proposed method.
  • Ya Li, Xinmei Tian, Mingli Song, Dacheng Tao
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    ABSTRACT: With the explosive growth of the use of imagery, visual recognition plays an important role in many applications and attracts increasing research attention. Given several related tasks, single-task learning learns each task separately and ignores the relationships among these tasks. Different from single-task learning, multi-task learning can explore more information to learn all tasks jointly by using relationships among these tasks. In this paper, we propose a novel multi-task learning model based on the proximal support vector machine. The proximal support vector machine uses the large-margin idea as does the standard Support Vector Machines but with looser constraints and much lower computational cost. Our multi-task proximal support vector machine inherits the merits of the proximal support vector machine and achieves better performance compared with other popular multi-task learning models. Experiments are conducted on several multi-task learning datasets, including two classification datasets and one regression dataset. All results demonstrate the effectiveness and efficiency of our proposed multi-task proximal support vector machine.
    Pattern Recognition 02/2015; DOI:10.1016/j.patcog.2015.01.014 · 2.58 Impact Factor
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    ABSTRACT: Graph Laplacian has been widely exploited in traditional graph-based semisupervised learning (SSL) algorithms to regulate the labels of examples that vary smoothly on the graph. Although it achieves a promising performance in both transductive and inductive learning, it is not effective for handling ambiguous examples (shown in Fig. 1). This paper introduces deformed graph Laplacian (DGL) and presents label prediction via DGL (LPDGL) for SSL. The local smoothness term used in LPDGL, which regularizes examples and their neighbors locally, is able to improve classification accuracy by properly dealing with ambiguous examples. Theoretical studies reveal that LPDGL obtains the globally optimal decision function, and the free parameters are easy to tune. The generalization bound is derived based on the robustness analysis. Experiments on a variety of real-world data sets demonstrate that LPDGL achieves top-level performance on both transductive and inductive settings by comparing it with popular SSL algorithms, such as harmonic functions, AnchorGraph regularization, linear neighborhood propagation, Laplacian regularized least square, and Laplacian support vector machine.
    IEEE transactions on neural networks and learning systems 01/2015; DOI:10.1109/TNNLS.2014.2376936 · 4.37 Impact Factor
  • Changxing Ding, Chang Xu, Dacheng Tao
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    ABSTRACT: Face images captured in unconstrained environments usually contain significant pose variation, which dramatically degrades the performance of algorithms designed to recognize frontal faces. This paper proposes a novel face identification framework capable of handling the full range of pose variations within 90 of yaw. The proposed framework first transforms the original pose-invariant face recognition problem into a partial frontal face recognition problem. A robust patch-based face representation scheme is then developed to represent the synthesized partial frontal faces. For each patch, a transformation dictionary is learnt under the proposed multitask learning scheme. The transformation dictionary transforms the features of different poses into a discriminative subspace. Lastly, face matching is performed at patch level rather than at the holistic level. Extensive and systematic experimentation on FERET, CMU-PIE, and Multi-PIE databases shows that the proposed method consistently outperforms single-task based baselines as well as state-of-the-art methods for the pose problem. We further extend the proposed algorithm for the unconstrained face verification problem and achieve top level performance on the challenging LFW dataset.
    IEEE Transactions on Image Processing 01/2015; 24(3). DOI:10.1109/TIP.2015.2390959 · 3.11 Impact Factor
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    ABSTRACT: Objective: Glaucoma is an irreversible chronic eye disease that leads to vision loss. As it can be slowed down through treatment, detecting the disease in time is important. However, many patients are unaware of the disease because it progresses slowly without easily noticeable symptoms. Currently, there is no effective method for low cost population-based glaucoma detection or screening. Recent studies have shown that automated optic nerve head assessment from 2D retinal fundus images is promising for low cost glaucoma screening. In this paper, we propose a method for cup to disc ratio (CDR) assessment using 2D retinal fundus images. Methods: In the proposed method, the optic disc is first segmented and reconstructed using a novel sparse dissimilarity-constrained coding (SDC) approach which considers both the dissimilarity constraint and the sparsity constraint from a set of reference discs with known CDRs. Subsequently, the reconstruction coefficients from the SDC are used to compute the CDR for the testing disc. Results: The proposed method has been tested for CDR assessment in a database of 650 images with CDRs manually measured by trained professionals previously. Experimental results show an average CDR error of 0.064 and correlation coefficient of 0.67 compared with the manual CDRs, better than the state-of-theart methods. Our proposed method has also been tested for glaucoma screening. The method achieves areas under curve of 0.83 and 0.88 on datasets of 650 and 1676 images, respectively, outperforming other methods. Conclusion: The proposed method achieves good accuracy for glaucoma detection. Significance: The method has a great potential to be used for large-scale populationbased glaucoma screening.
    IEEE transactions on bio-medical engineering 01/2015; DOI:10.1109/TBME.2015.2389234 · 2.15 Impact Factor
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    ABSTRACT: Example learning-based super-resolution (SR) algorithms show promise for restoring a high-resolution (HR) image from a single low-resolution (LR) input. The most popular approaches, however, are either time- or space-intensive, which limits their practical applications in many resource-limited settings. In this paper we propose a novel computationally efficient single image SR method that learns multiple linear mappings (MLM) to directly transform LR feature subspaces into HR subspaces. Specifically, we first partition the large non-linear feature space of LR images into a cluster of linear subspaces. Multiple LR subdictionaries are then learned, followed by inferring the corresponding HR subdictionaries based on the assumption that the LR-HR features share the same representation coefficients. We establish MLM from the input LR features to the desired HR outputs in order to achieve fast yet stable SR recovery. Furthermore, in order to suppress displeasing artifacts generated by the MLM-based method, we apply a fast non-local means (NLM) algorithm to construct a simple yet effective similaritybased regularization term for SR enhancement. Experimental results indicate that our approach is both quantitatively and qualitatively superior to other application-oriented SR methods, while maintaining relatively low time and space complexity.
    IEEE Transactions on Image Processing 01/2015; 24(3). DOI:10.1109/TIP.2015.2389629 · 3.11 Impact Factor
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    ABSTRACT: Whereas the transform coding algorithms have been proved to be efficient and practical for grey-level and color images compression, they could not directly deal with the hyperspectral images (HSI) by simultaneously considering both the spatial and spectral domains of the data cube. The aim of this paper is to present an HSI compression and reconstruction method based on the multi-dimensional or tensor data processing approach. By representing the observed hyperspectral image cube to a 3-order-tensor, we introduce a tensor decomposition technology to approximately decompose the original tensor data into a core tensor multiplied by a factor matrix along each mode. Thus, the HSI is compressed to the core tensor and could be reconstructed by the multi-linear projection via the factor matrices. Experimental results on particular applications of hyperspectral remote sensing images such as unmixing and detection suggest that the reconstructed data by the proposed approach significantly preserves the HSI׳s data quality in several aspects.
    Neurocomputing 01/2015; 147:358–363. DOI:10.1016/j.neucom.2014.06.052 · 2.01 Impact Factor
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    ABSTRACT: In this paper, we introduce an efficient tensor to vector projection algorithm for human gait feature representation and recognition. The proposed approach is based on the multi-dimensional or tensor signal processing technology, which finds a low-dimensional tensor subspace of original input gait sequence tensors while most of the data variation has been well captured. In order to further enhance the class separability and avoid the potential overfitting, we adopt a discriminative locality preserving projection with sparse regularization to transform the refined tensor data to the final vector feature representation for subsequent recognition. Numerous experiments are carried out to evaluate the effectiveness of the proposed sparse and discriminative tensor to vector projection algorithm, and the proposed method achieves good performance for human gait recognition using the sequences from the University of South Florida (USF) HumanID Database.
    Signal Processing 01/2015; 106:245–252. DOI:10.1016/j.sigpro.2014.08.005 · 2.24 Impact Factor
  • Pattern Recognition 01/2015; DOI:10.1016/j.patcog.2014.12.016 · 2.58 Impact Factor
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    ABSTRACT: Local binary patterns (LBP) achieve great success in texture analysis, however they are not robust to noise. The two reasons for such disadvantage of LBP schemes are (1) they encode the texture spatial structure based only on local information which is sensitive to noise and (2) they use exact values as the quantization thresholds, which make the extracted features sensitive to small changes in the input image. In this paper, we propose a noise-robust adaptive hybrid pattern (AHP) for noised texture analysis. In our scheme, two solutions from the perspective of texture description model and quantization algorithm have been developed to reduce the feature׳s noise sensitiveness. First, a hybrid texture description model is proposed. In this model, the global texture spatial structure which is depicted by a global description model is encoded with the primitive microfeature for texture description. Second, we develop an adaptive quantization algorithm in which equal probability quantization is utilized to achieve the maximum partition entropy. Higher noise-tolerance can be obtained with the minimum lost information in the quantization process. The experimental results of texture classification on two texture databases with three different types of noise show that our approach leads significant improvement in noised texture analysis. Furthermore, our scheme achieves state-of-the-art performance in noisy face recognition.
    Pattern Recognition 01/2015; DOI:10.1016/j.patcog.2015.01.001 · 2.58 Impact Factor
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    ABSTRACT: Distance metric learning (DML) is successful in discovering intrinsic relations in data. However, most algorithms are computationally demanding when the problem size becomes large. In this paper, we propose a discriminative metric learning algorithm, develop a distributed scheme learning metrics on moderate-sized subsets of data, and aggregate the results into a global solution. The technique leverages the power of parallel computation. The algorithm of the aggregated DML (ADML) scales well with the data size and can be controlled by the partition. We theoretically analyze and provide bounds for the error induced by the distributed treatment. We have conducted experimental evaluation of the ADML, both on specially designed tests and on practical image annotation tasks. Those tests have shown that the ADML achieves the state-of-the-art performance at only a fraction of the cost incurred by most existing methods.
    IEEE transactions on neural networks and learning systems 12/2014; DOI:10.1109/TNNLS.2014.2377211 · 4.37 Impact Factor
  • Chen Gong, Dacheng Tao, Keren Fu, Jie Yang
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    ABSTRACT: How to propagate the label information from labeled examples to unlabeled examples is a critical problem for graph-based semisupervised learning. Many label propagation algorithms have been developed in recent years and have obtained promising performance on various applications. However, the eigenvalues of iteration matrices in these algorithms are usually distributed irregularly, which slow down the convergence rate and impair the learning performance. This paper proposes a novel label propagation method called Fick's law assisted propagation (FLAP). Unlike the existing algorithms that are directly derived from statistical learning, FLAP is deduced on the basis of the theory of Fick's First Law of Diffusion, which is widely known as the fundamental theory in fluid-spreading. We prove that FLAP will converge with linear rate and show that FLAP makes eigenvalues of the iteration matrix distributed regularly. Comprehensive experimental evaluations on synthetic and practical datasets reveal that FLAP obtains encouraging results in terms of both accuracy and efficiency.
    IEEE transactions on neural networks and learning systems 12/2014; DOI:10.1109/TNNLS.2014.2376963 · 4.37 Impact Factor
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    ABSTRACT: Technical analysis with numerous indicators and patterns has been regarded as important evidence for making trading decisions in financial markets. However, it is extremely difficult for investors to find useful trading rules based on numerous technical indicators. This paper innovatively proposes the use of biclustering mining to discover effective technical trading patterns that contain a combination of indicators from historical financial data series. This is the first attempt to use biclustering algorithm on trading data. The mined patterns are regarded as trading rules and can be classified as three trading actions (i.e., the buy, the sell, and no-action signals) with respect to the maximum support. A modified K nearest neighborhood (K-NN) method is applied to classification of trading days in the testing period. The proposed method [called biclustering algorithm and the K nearest neighbor (BIC-K-NN)] was implemented on four historical datasets and the average performance was compared with the conventional buy-and-hold strategy and three previously reported intelligent trading systems. Experimental results demonstrate that the proposed trading system outperforms its counterparts and will be useful for investment in various financial markets.
    12/2014; DOI:10.1109/TCYB.2014.2370063
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    ABSTRACT: Random Forest is a very important classifier with applications in various machine learning tasks, but its promising performance heavily relies on the size of labeled training data. In this paper, we investigate constructing Random Forests with a small size of labeled data and find that the performance bottleneck is located in the node splitting procedures; hence existing solutions fail to properly partition the feature space if there are insufficient training data. To achieve robust node splitting with insufficient data, we present semi-supervised splitting to overcome this limitation by splitting nodes with the guidance of both labeled and abundant unlabeled data. In particular, an accurate quality measure of node splitting is obtained by carrying out the kernel-based density estimation whereby a multi-class version of asymptotic mean integrated squared error criterion is proposed to adaptively select the optimal bandwidth of the kernel. To avoid the curse of dimensionality, we project the data points from the original high-dimensional feature space onto a low-dimensional subspace before estimation. A unified optimization framework is proposed to select a coupled pair of subspace and separating hyperplane such that the smoothness of the subspace and the quality of the splitting are guaranteed simultaneously. Our algorithm efficiently avoids overfitting caused by bad initialization and local maxima when compared with conventional margin maximizationbased semi-supervised methods. We demonstrate the effectiveness of the proposed algorithm by comparing it with state-of-the-art supervised and semi-supervised algorithms for typical computer vision applications such as object categorization, face recognition and image segmentation on publicly available datasets.
    IEEE Transactions on Image Processing 12/2014; 24(1). DOI:10.1109/TIP.2014.2378017 · 3.11 Impact Factor
  • Jun Yu, Yong Rui, Yuan Yan Tang, Dacheng Tao
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    ABSTRACT: How do we find all images in a larger set of images which have a specific content? Or estimate the position of a specific object relative to the camera? Image classification methods, like support vector machine (supervised) and transductive support vector machine (semi-supervised), are invaluable tools for the applications of content-based image retrieval, pose estimation, and optical character recognition. However, these methods only can handle the images represented by single feature. In many cases, different features (or multiview data) can be obtained, and how to efficiently utilize them is a challenge. It is inappropriate for the traditionally concatenating schema to link features of different views into a long vector. The reason is each view has its specific statistical property and physical interpretation. In this paper, we propose a high-order distance-based multiview stochastic learning (HD-MSL) method for image classification. HD-MSL effectively combines varied features into a unified representation and integrates the labeling information based on a probabilistic framework. In comparison with the existing strategies, our approach adopts the high-order distance obtained from the hypergraph to replace pairwise distance in estimating the probability matrix of data distribution. In addition, the proposed approach can automatically learn a combination coefficient for each view, which plays an important role in utilizing the complementary information of multiview data. An alternative optimization is designed to solve the objective functions of HD-MSL and obtain different views on coefficients and classification scores simultaneously. Experiments on two real world datasets demonstrate the effectiveness of HD-MSL in image classification.
    12/2014; 44(12):2431-42. DOI:10.1109/TCYB.2014.2307862
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    Tongliang Liu, Dacheng Tao
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    ABSTRACT: In this paper, we study a classification problem in which sample labels are randomly corrupted. In this scenario, there is an unobservable sample with noise-free labels. However, before being observed, the true labels are independently flipped with a probability $\rho\in[0,0.5)$, and the random label noise can be class-conditional. Here, we address two fundamental problems raised by this scenario. The first is how to best use the abundant surrogate loss functions designed for the traditional classification problem when there is label noise. We prove that any surrogate loss function can be used for classification with noisy labels by using importance reweighting, with consistency assurance that the label noise does not ultimately hinder the search for the optimal classifier of the noise-free sample. The other is the open problem of how to obtain the noise rate $\rho$. We show that the rate is upper bounded by the conditional probability $P(y|x)$ of the noisy sample. Consequently, the rate can be estimated, because the upper bound can be easily reached in classification problems. Experimental results on synthetic and real datasets confirm the efficiency of our methods.

Publication Stats

7k Citations
624.60 Total Impact Points


  • 2010–2015
    • University of Technology Sydney 
      • Centre for Quantum Computation and Intelligent Systems (QCIS)
      Sydney, New South Wales, Australia
    • Shanghai Jiao Tong University
      Shanghai, Shanghai Shi, China
  • 2013–2014
    • University of Petroleum (East China)
      Tsingtao, Shandong Sheng, China
    • China University of Petroleum
      Ch’ang-p’ing-ch’ü, Beijing, China
  • 2011–2013
    • National University of Defense Technology
      • National Key Laboratory of Parallel and Distributed Processing
      Ch’ang-sha-shih, Hunan, China
    • École Polytechnique Fédérale de Lausanne
      • Section d'informatique
      Lausanne, VD, Switzerland
    • Northeast Institute of Geography and Agroecology
      • National Pattern Recognition Laboratory
      Beijing, Beijing Shi, China
    • Wuhan University
      • State Key Laboratory of Information engineering in Surveying, Mapping and Remote Sensing
      Wuhan, Hubei, China
    • State Key Laboratory Of Transient Optics And Photonics
      Ch’ang-an, Shaanxi, China
    • Xiamen University
      • Department of Computer Science
      Xiamen, Fujian, China
    • Peking University
      • School of Electronics Engineering and Computer Science
      Peping, Beijing, China
  • 2008–2012
    • Xidian University
      • School of Life Sciences and Technology
      Ch’ang-an, Shaanxi, China
    • Zhejiang University
      • College of Computer Science and Technology
      Hangzhou, Zhejiang Sheng, China
    • University of Vermont
      • Department of Computer Science
      Burlington, Vermont, United States
    • Tianjin University
      • Department of Electronic Information Engineering
      T’ien-ching-shih, Tianjin Shi, China
  • 1970–2012
    • Nanyang Technological University
      • School of Computer Engineering
      Tumasik, Singapore
  • 2008–2011
    • Chinese Academy of Sciences
      • • Xi'an Institute of Optics and Precision Mechanics
      • • Shenzhen Institutes of Advanced Technology
      • • National Pattern Recognition Laboratory
      Peping, Beijing, China
  • 2009–2010
    • Aston University
      • School of Engineering and Applied Science
      Birmingham, England, United Kingdom
    • Hong Kong Baptist University
      Chiu-lung, Kowloon City, Hong Kong
    • Western Kentucky University
      Kentucky, United States
    • Brunel University
      • School of Engineering and Design
      अक्सब्रिज, England, United Kingdom
    • National Space Science
      Peping, Beijing, China
  • 2007–2010
    • The University of Hong Kong
      • Department of Computer Science
      Hong Kong, Hong Kong
  • 2007–2009
    • The Hong Kong Polytechnic University
      • Department of Computing
      Hong Kong, Hong Kong
  • 2005–2009
    • Birkbeck, University of London
      • Department of Computer Science and Information Systems
      Londinium, England, United Kingdom
    • University of London
      Londinium, England, United Kingdom
  • 2006–2008
    • The University of Sheffield
      • Department of Electronic and Electrical Engineering
      Sheffield, ENG, United Kingdom
    • Tongji Hospital
      Wu-han-shih, Hubei, China
  • 2004–2005
    • The Chinese University of Hong Kong
      • Department of Information Engineering
      Hong Kong, Hong Kong