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Professor Jie Yang

Adjunct Professor, School of Computing and Communications


Zhang, F., Li, J., Li, F., Xu, M., Xu, R. & He, X. 2015, 'Community detection based on links and node features in social networks', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 418-429.
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© Springer International Publishing Switzerland 2015. Community detection is a significant but challenging task in the field of social network analysis. Many effective methods have been proposed to solve this problem. However, most of them are mainly based on the topological structure or node attributes. In this paper, based on SPAEM [1], we propose a joint probabilistic model to detect community which combines node attributes and topological structure. In our model, we create a novel feature-based weighted network, within which each edge weight is represented by the node feature similarity between two nodes at the end of the edge. Then we fuse the original network and the created network with a parameter and employ expectation-maximization algorithm (EM) to identify a community. Experiments on a diverse set of data, collected from Facebook and Twitter, demonstrate that our algorithm has achieved promising results compared with other algorithms.
Zhu, L., Cao, L. & Yang, J. 2012, 'Multiobjective evolutionary algorithm-based soft subspace clustering', 2012 IEEE Congress on Evolutionary Computation, CEC 2012.
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In this paper, a multiobjective evolutionary algorithm based soft subspace clustering, MOSSC, is proposed to simultaneously optimize the weighting within-cluster compactness and weighting between-cluster separation incorporated within two different clustering validity criteria. The main advantage of MOSSC lies in the fact that it effectively integrates the merits of soft subspace clustering and the good properties of the multiobjective optimization-based approach for fuzzy clustering. This makes it possible to avoid trapping in local minima and thus obtain more stable clustering results. Substantial experimental results on both synthetic and real data sets demonstrate that MOSSC is generally effective in subspace clustering and can achieve superior performance over existing state-of-the-art soft subspace clustering algorithms. © 2012 IEEE.
Zhu, L., Cao, L. & Yang, J. 2011, 'Soft subspace clustering with competitive agglomeration', IEEE International Conference on Fuzzy Systems, pp. 691-698.
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In this paper, two novel soft subspace clustering algorithms, namely fuzzy weighting subspace clustering with competitive agglomeration (FWSCA) and entropy weighting subspace clustering with competitive agglomeration (EWSCA), are proposed to overcome the problems of the unknown number of clusters and the initialization of prototypes for soft subspace clustering. The main advantage of FWSCA and EWSCA lies in the fact that they effectively integrate the merits of soft subspace clustering and the good properties of fuzzy clustering with competitive agglomeration. This makes it possible to obtain the appropriate number of clusters during the clustering progress. Moreover, FWSCA and EWSCA algorithms can converge regardless of the initial number of clusters and initialization. Substantial experimental results on both synthetic and real data sets demonstrate the effectiveness of FWSCA and EWSCA in addressing the two problems. © 2011 IEEE.
chen, X., He, S., Yang, J. & Wu, Q. 2011, 'An Effective Document Image Deblurring Algorithm', Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference on, IEEE Conference on Computer Vision and Pattern Recognition, IEEE Computer Society, Colorado Springs, pp. 369-376.
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Deblurring camera-based document image is an important task in digital document processing, since it can improve both the accuracy of optical character recognition systems and the visual quality of document images. Traditional deblurring algorithms have been proposed to work for natural-scene images. However the natural-scene images are not consistent with document images. In this paper, the distinct characteristics of document images are investigated. We propose a content-aware prior for document image deblurring. It is based on document image foreground segmentation. Besides, an upper-bound constraint combined with total variation based method is proposed to suppress the rings in the deblurred image. Comparing with the traditional general purpose deblurring methods, the proposed deblurring algorithm can produce more pleasing results on document images. Encouraging experimental results demonstrate the efficacy of the proposed method.

Journal articles

Dong, X., Liu, E., Yang, J. & Wu, Q. 2013, 'MEGH: A new affine invariant descriptor', KSII Transactions on Internet and Information Systems, vol. 7, no. 7, pp. 1690-1704.
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An affine invariant descriptor is proposed, which is able to well represent the affine covariant regions. Estimating main orientation is still problematic in many existing method, such as SIFT (scale invariant feature transform) and SURF (speeded up robust features). Instead of aligning the estimated main orientation, in this paper ellipse orientation is directly used. According to ellipse orientation, affine covariant regions are firstly divided into 4 sub-regions with equal angles. Since affine covariant regions are divided from the ellipse orientation, the divided sub-regions are rotation invariant regardless the rotation, if any, of ellipse. Meanwhile, the affine covariant regions are normalized into a circular region. In the end, the gradients of pixels in the circular region are calculated and the partition-based descriptor is created by using the gradients. Compared with the existing descriptors including MROGH, SIFT, GLOH, PCA-SIFT and spin images, the proposed descriptor demonstrates superior performance according to extensive experiments. © 2013 KSII.