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Chen F.,Fuzhou University | Zhang L.,Hong Kong Polytechnic University | Yu H.,Zhejiang University | Yu H.,State Key Laboratory of CAD and CG
Proceedings of the IEEE International Conference on Computer Vision

Natural image modeling plays a key role in many vision problems such as image denoising. Image priors are widely used to regularize the denoising process, which is an illposed inverse problem. One category of denoising methods exploit the priors (e.g., TV, sparsity) learned from external clean images to reconstruct the given noisy image, while another category of methods exploit the internal prior (e.g., self-similarity) to reconstruct the latent image. Though the internal prior based methods have achieved impressive denoising results, the improvement of visual quality will become very difficult with the increase of noise level. In this paper, we propose to exploit image external patch prior and internal self-similarity prior jointly, and develop an external patch prior guided internal clustering algorithm for image denoising. It is known that natural image patches form multiple subspaces. By utilizing Gaussian mixture models (GMMs) learning, image similar patches can be clustered and the subspaces can be learned. The learned GMMs from clean images are then used to guide the clustering of noisypatches of the input noisy images, followed by a low-rank approximation process to estimate the latent subspace for image recovery. Numerical experiments show that the proposed method outperforms many state-of-the-art denoising algorithms such as BM3D and WNNM. © 2015 IEEE. Source

Peng P.,State Key Laboratory of CAD and CG | Shou L.,State Key Laboratory of CAD and CG | Shou L.,Zhejiang University | Chen K.,State Key Laboratory of CAD and CG | And 2 more authors.
SIGIR 2013 - Proceedings of the 36th International ACM SIGIR Conference on Research and Development in Information Retrieval

This paper presents a framework called Knowing Camera for real-time recognizing places-of-interest in smartphone photos, with the availability of online geotagged images of such places. We propose a probabilistic field-of-view model which captures the uncertainty in camera sensor data. This model can be used to retrieve a set of candidate images. The visual similarity computation of the candidate images relies on the sparse coding technique. We also propose an ANN filtering technique to speedup the sparse coding. The final ranking combines an uncertain geometric relevance with the visual similarity. Our preliminary experiments conducted in an urban area of a large city show promising results. The most distinguishing feature of our framework is its ability to perform well in contaminated, real-world online image database. Besides, our framework is highly scalable as it does not incur any complex data structure. Copyright © 2013 ACM. Source

Chen F.,Zhejiang University | Chen F.,Jimei University | Yu H.,Zhejiang University | Yu H.,State Key Laboratory of CAD and CG | And 2 more authors.
Pattern Analysis and Applications

In this paper, a robust sparse kernel density estimation based on the reduced set density estimator is proposed. The key idea is to induce randomness to the plug-in estimation of weighting coefficients. The random fluctuations can inhibit these small nonzero weighting coefficients to cluster in regions of space with greater probability mass. By sequential minimal optimization, these coefficients are merged into a few larger weighting coefficients. Experimental studies show that the proposed model is superior to several related methods both in sparsity and accuracy of the estimation. Moreover, the proposed density estimation is extensively validated on novelty detection and binary classification. © 2013 Springer-Verlag London. Source

Chen F.,Zhejiang University | Chen F.,Jimei University | Yu H.,Zhejiang University | Yu H.,State Key Laboratory of CAD and CG | And 2 more authors.
Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition

In this paper we introduce a new shape-driven approach for object segmentation. Given a training set of shapes, we first use deep Boltzmann machine to learn the hierarchical architecture of shape priors. This learned hierarchical architecture is then used to model shape variations of global and local structures in an energetic form. Finally, it is applied to data-driven variational methods to perform object extraction of corrupted data based on shape probabilistic representation. Experiments demonstrate that our model can be applied to dataset of arbitrary prior shapes, and can cope with image noise and clutter, as well as partial occlusions. © 2013 IEEE. Source

Chen F.,Zhejiang University | Chen F.,Jimei University | Hu R.,Zhejiang University | Yu H.,Zhejiang University | And 2 more authors.
Journal of Visual Communication and Image Representation

In this paper, a nonparametric statistical shape model based on shape probabilistic representation is proposed for object segmentation. Given a set of training shapes, Cremers et al.'s probabilistic method is adopted to represent the shape, and then principal components analysis (PCA) on shape probabilistic representation is computed to capture the variation of the training shapes. To encode complex shape variation in training set, reduced set density estimator is used to model nonlinear shape distributions in a finite-dimensional subspace. This statistical shape prior is integrated to convex segmentation functional to guide the evolving contour to the object of interest. In addition, in contrast to the commonly used signed distance functions, PCA on shape probabilistic representation needs less number of eigenmodes to capture certain details of the training shapes. Numerical experiments show promising results and the potential of the model for object segmentation. © 2012 Elsevier Inc. All rights reserved. Source

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