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Professor Jie Yang

Adjunct Professor, School of Computing and Communications
 

Conference Papers

Zhu, L., Cao, L. & Yang, J. 2012, 'Multiobjective evolutionary algorithm-based soft subspace clustering', IEEE Congress on Evolutionary Computation, Brisbane, Australia, June 2012 in IEEE Congress on Evolutionary Computation 2012, ed Abbass, H.; Essam, D.; Sarker, R., IEEE, Piscataway, USA, pp. 1-8.
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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
chen, x., He, X.S., Yang, J. & Wu, Q. 2011, 'An Effective Document Image Deblurring Algorithm', IEEE Conference on Computer Vision and Pattern Recognition, Colorado Springs, June 2011 in Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference on, ed Terrance Boult, Shmuel Peleg,Pedro Felzenszwalb, David Forsyth and Pascal Fua, IEEE Computer Society, Piscataway, USA, 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.
Zhu, L., Cao, L. & Yang, J. 2011, 'Soft subspace clustering with competitive agglomeration', IEEE International Conference on Fuzzy Systems, Taipei, June 2011 in IEEE International Conference on Fuzzy Systems 2011, ed Cao, L, IEEE, Taiwan, 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

Journal Articles

Gong, C., Fu, K., Tu, E., Yang, J. & He, X.S. 2013, 'Robust Object Tracking Using Linear Neighborhood Propagation', Journal of Electronic Imaging, vol. 22, no. 1, pp. 013015-1-013015-10.
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Object tracking is widely used in many applications such as intelligent surveillance, scene understanding, and behavior analysis. Graph-based semisupervised learning has been introduced to deal with specific tracking problems. However, existing algorithms following this idea solely focus on the pairwise relationship between samples and hence could decrease the classification accuracy for unlabeled samples. On the contrary, we regard tracking as a one-class classification issue and present a novel graph-based semisupervised tracker. The proposed tracker uses linear neighborhood propagation, which aims to exploit the local information around each data point. Moreover, the manifold structure embedded in the whole sample set is discovered to allow the tracker to better model the target appearance, which is crucial to resisting the appearance variations of the object. Experiments on some public-domain sequences show that the proposed tracker can exhibit reliable tracking performance in the presence of partial occlusions, complicated background, and appearance changes
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 robu
Du, C., Yang, J., Wu, Q. & Zhang, T.T. 2009, 'Face Recognition Using Message Passing Based Clustering Method', Journal of Visual Communication, vol. 20, no. 8, pp. 608-613.
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Traditional subspace analysis methods are inefficient and tend to be affected by noise as they compare the test image to all training images, especifically when there are large numbers of training images. To solve such problem, we propose a fast face recognition (FR) technique called APLDA by combining a novel clustering method affinity propagation (AP) with linear discriminant analysis (LDA). By using AP on the reduced features derived from LDA, a representative face image for each subject can be reached. Thus, our APLDA uses only the representative images rather than all training images for identification. Obviously, APLDA is much more computationally efficient than Fisherface. Also, unlike Fisherface who uses pattern classifier for identification, APLDA performs the identification using AP once again to cluster the test image into one of the representative images. Experimental results also indicate that APLDA outperforms Fisherface in terms of recognition rate.