Synchromedia Laboratory for Multimedia Communication in Telepresence

Montréal, Canada

Synchromedia Laboratory for Multimedia Communication in Telepresence

Montréal, Canada
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Zhong G.,Synchromedia Laboratory for Multimedia Communication in Telepresence | Cheriet M.,Synchromedia Laboratory for Multimedia Communication in Telepresence
Neural Computation | Year: 2014

We present a supervised model for tensor dimensionality reduction, which is called large margin low rank tensor analysis (LMLRTA). In contrast to traditional vector representation-based dimensionality reduction methods, LMLRTA can take any order of tensors as input. And unlike previous tensor dimensionality reductionmethods, which can learn only the low-dimensional embeddings with a priori specified dimensionality, LMLRTA can automatically and jointly learn the dimensionality and the low-dimensional representations from data. Moreover, LMLRTAdelivers low rank projection matrices, while it encourages data of the same class to be close and of different classes to be separated by a large margin of distance in the low-dimensional tensor space. LMLRTAcan be optimized using an iterative fixed-point continuation algorithm, which is guaranteed to converge to a local optimal solution of the optimization problem. We evaluate LMLRTA on an object recognition application, where the data are represented as 2D tensors, and a face recognition application, where the data are represented as 3D tensors. Experimental results show the superiority of LMLRTA over state-of-the-art approaches. © 2014 Massachusetts Institute of Technology.


Berthiaume V.,Laboratory for Imagery | Cheriet M.,Synchromedia Laboratory for Multimedia Communication in Telepresence
Electronic Letters on Computer Vision and Image Analysis | Year: 2012

Any statistical pattern recognition system includes a feature extraction component. For character patterns, several feature families have been tested, such as the Fourier-Wavelet Descriptors. We are proposing here a generalization of this family: the Fourier-Packet Descriptors. We have selected sets of these features and tested them on handwritten digits: the error rate was 1.55% with a polynomial classifier for a 70 features set and 1.97% with a discriminative learning quadratic discriminant function for a 40 features set.


Adankon M.M.,Synchromedia Laboratory for Multimedia Communication in Telepresence | Cheriet M.,Synchromedia Laboratory for Multimedia Communication in Telepresence
Pattern Recognition | Year: 2011

In this paper, we propose to reinforce the Self-Training strategy in semi-supervised mode by using a generative classifier that may help to train the main discriminative classifier to label the unlabeled data. We call this semi-supervised strategy Help-Training and apply it to training kernel machine classifiers as support vector machines (SVMs) and as least squares support vector machines. In addition, we propose a model selection strategy for semi-supervised training. Experimental results on both artificial and real problems demonstrate that Help-Training outperforms significantly the standard Self-Training. Moreover, compared to other semi-supervised methods developed for SVMs, our Help-Training strategy often gives the lowest error rate. © 2011 Elsevier Ltd. All rights reserved.


Moghaddam R.F.,Synchromedia Laboratory for Multimedia Communication in Telepresence | Cheriet M.,Synchromedia Laboratory for Multimedia Communication in Telepresence
Pattern Recognition | Year: 2011

A patch-based non-local restoration and reconstruction method for preprocessing degraded document images is introduced. The method collects relative data from the whole input image, while the image data are first represented by a content-level descriptor based on patches. This patch-equivalent representation of the input image is then corrected based on similar patches identified using a modified genetic algorithm (GA) resulting in a low computational load. The corrected patch-equivalent is then converted to the output restored image. The fact that the method uses the patches at the content level allows it to incorporate high-level restoration in an objective and self-sufficient way. The method has been applied to several degraded document images, including the DIBCO'09 contest dataset with promising results. © 2010 Elsevier Ltd. All rights reserved.

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