Rémi Flamary

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I am associate professor at Nice-Sophia Antipolis University in the Departement of Electronics and in the Lagrange Laboratory. This laboratory is part of the Observatoire de la Côte d'Azur. I was previously a PhD student and teaching assistant at the LITIS Laboratory and my PhD advisor was Alain Rakotomamonjy at Rouen University.

On this website, you can find a list of my publications and download the corresponding software/code. Some of my french teaching material is also available.

Research Interests

• Machine Learning
• Statistical signal processing
• Classification and segmentation of signals and images
• Filter learning, image reconstruction
• Sparse and non-convex optimization
• Applications
• Biomedical engineering, Brain-Computer Interfaces
• Remote sensing and hyperspectral Imaging
• Astronomical image processing

Wordcloud of my research interests.

Recent work

 D. Tuia, R. Flamary, M. Barlaud, "To be or not to be convex? A study on regularization in hyperspectral image classification", International Geoscience and Remote Sensing Symposium (IGARSS), 2015. Abstract: Hyperspectral image classification has long been dominated by convex models, which provide accurate decision functions exploiting all the features in the input space. However, the need for high geometrical details, which are often satisfied by using spatial filters, and the need for compact models (i.e. relying on models issued form reduced input spaces) has pushed research to study alternatives such as sparsity inducing regularization, which promotes models using only a subset of the input features. Although successful in reducing the number of active inputs, these models can be biased and sometimes offer sparsity at the cost of reduced accuracy. In this paper, we study the possibility of using non-convex regularization, which limits the bias induced by the regularization. We present and compare four regularizers, and then apply them to hyperspectral classification with different cost functions. BibTeX: @inproceedings{tuia2015tobe, author = {Tuia, D. and Flamary, R. and Barlaud, M.}, title = {To be or not to be convex? A study on regularization in hyperspectral image classification}, booktitle = {International Geoscience and Remote Sensing Symposium (IGARSS)}, year = {2015} } N. Courty, R. Flamary, D. Tuia, "Domain adaptation with regularized optimal transport", European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD), 2014. Abstract: We present a new and original method to solve the domain adaptation problem using optimal transport. By searching for the best transportation plan between the probability distribution functions of a source and a target domain, a non-linear and invertible transformation of the learning samples can be estimated. Any standard machine learning method can then be applied on the transformed set, which makes our method very generic. We propose a new optimal transport algorithm that incorporates label information in the optimization: this is achieved by combining an efficient matrix scaling technique together with a majoration of a non-convex regularization term. By using the proposed optimal transport with label regularization, we obtain significant increase in performance compared to the original transport solution. The proposed algorithm is computationally efficient and effective, as illustrated by its evaluation on a toy example and a challenging real life vision dataset, against which it achieves competitive results with respect to state-of-the-art methods. BibTeX: @inproceedings{courty2014domain, author = {Courty, N. and Flamary, R. and Tuia, D.}, title = {Domain adaptation with regularized optimal transport}, booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD)}, year = {2014} } A. Boisbunon, R. Flamary, A. Rakotomamonjy, A. Giros, J. Zerubia, "Large scale sparse optimization for object detection in high resolution images", IEEE Workshop in Machine Learning for Signal Processing (MLSP), 2014. Abstract: In this work, we address the problem of detecting objects in images by expressing the image as convolutions between activation matrices and dictionary atoms. The activation matrices are estimated through sparse optimization and correspond to the position of the objects. In particular, we propose an efficient algorithm based on an active set strategy that is easily scalable and can be computed in parallel. We apply it to a toy image and a satellite image where the aim is to detect all the boats in a harbor. These results show the benefit of using nonconvex penalties, such as the log-sum penalty, over the convex l1 penalty. BibTeX: @inproceedings{boisbunon2014largescale, author = {Boisbunon, A. and Flamary, R. and Rakotomamonjy, A. and Giros, A. and Zerubia, J.}, title = {Large scale sparse optimization for object detection in high resolution images}, booktitle = {IEEE Workshop in Machine Learning for Signal Processing (MLSP)}, year = {2014} } A. Boisbunon, R. Flamary, A. Rakotomamonjy, "Active set strategy for high-dimensional non-convex sparse optimization problems", International Conference on Acoustic, Speech and Signal Processing (ICASSP), 2014. Abstract: The use of non-convex sparse regularization has attracted much interest when estimating a very sparse model on high dimensional data. In this work we express the optimality conditions of the optimization problem for a large class of non-convex regularizers. From those conditions, we derive an efficient active set strategy that avoids the computing of unnecessary gradients. Numerical experiments on both generated and real life datasets show a clear gain in computational cost w.r.t. the state of the art when using our method to obtain very sparse solutions. BibTeX: @inproceedings{boisbunon2014active, author = {Boisbunon, A. and Flamary, R. and Rakotomamonjy, A.}, title = {Active set strategy for high-dimensional non-convex sparse optimization problems}, booktitle = {International Conference on Acoustic, Speech and Signal Processing (ICASSP)}, year = {2014} } E. Niaf, R. Flamary, A. Rakotomamonjy, O. Rouvière, C. Lartizien, "SVM with feature selection and smooth prediction in images: application to CAD of prostate cancer", IEEE International Conference on Image Processing (ICIP), 2014. Abstract: We propose a new computer-aided detection scheme for prostate cancer screening on multiparametric magnetic resonance (mp-MR) images. Based on an annotated training database of mp-MR images from thirty patients, we train a novel support vector machine (SVM)-inspired classifier which simultaneously learns an optimal linear discriminant and a subset of predictor variables (or features) that are most relevant to the classification task, while promoting spatial smoothness of the malignancy prediction maps. The approach uses a $\ell_1$-norm in the regularization term of the optimization problem that rewards sparsity. Spatial smoothness is promoted via an additional cost term that encodes the spatial neighborhood of the voxels, to avoid noisy prediction maps. Experimental comparisons of the proposed $\ell_1$-Smooth SVM scheme to the regular $\ell_2$-SVM scheme demonstrate a clear visual and numerical gain on our clinical dataset. BibTeX: @inproceedings{niaf2014svmsmooth, author = {Niaf, E. and Flamary, R. and Rakotomamonjy, A. and Rouvière, O. and Lartizien, C.}, title = {SVM with feature selection and smooth prediction in images: application to CAD of prostate cancer}, booktitle = {IEEE International Conference on Image Processing (ICIP)}, year = {2014} }

News

PhD thesis proposal

2015-04-03

Cedric Richard an I are proposing a PhD thesis subject starting in 2015 on the subject of Distributed estimation over multitask networks.

If you are interested, contact Cédric or me before June 6.

For more details see the complete proposal.

BasMatI Summer School

2015-02-13

We are organizing a french summer school with Céline Theys, David Mary et Claude Aime about Mathemathics for signal and image processing in astronomy You can find pore information on the website.

Best paper at PCV 2014

2014-09-10

Our paper has been chosen for a best paper award at the Photogrammetric Computer Vision symposium (PCV 2014).

 D. Tuia, N. Courty, R. Flamary, "A group-lasso active set strategy for multiclass hyperspectral image classification", Photogrammetric Computer Vision (PCV), 2014. Abstract: Hyperspectral images have a strong potential for landcover/landuse classification, since the spectra of the pixels can highlight subtle differences between materials and provide information beyond the visible spectrum. Yet, a limitation of most current approaches is the hypothesis of spatial independence between samples: images are spatially correlated and the classification map should exhibit spatial regularity. One way of integrating spatial smoothness is to augment the input spectral space with filtered versions of the bands. However, open questions remain, such as the selection of the bands to be filtered, or the filterbank to be used. In this paper, we consider the entirety of the possible spatial filters by using an incremental feature learning strategy that assesses whether a candidate feature would improve the model if added to the current input space. Our approach is based on a multiclass logistic classifier with group-lasso regularization. The optimization of this classifier yields an optimality condition, that can easily be used to assess the interest of a candidate feature without retraining the model, thus allowing drastic savings in computational time. We apply the proposed method to three challenging hyperspectral classification scenarios, including agricultural and urban data, and study both the ability of the incremental setting to learn features that always improve the model and the nature of the features selected. BibTeX: @inproceedings{tuia2014grouplasso, author = {Tuia, D. and Courty, N. and Flamary, R.}, title = {A group-lasso active set strategy for multiclass hyperspectral image classification}, booktitle = {Photogrammetric Computer Vision (PCV)}, editor = {}, year = {2014} } 

This is a joint work with Devis Tuia and Nicolas Courty.