MALEGRA - Multimodal Signal Processing and Machine Learning on Graphs

Type Start End
National Jan 2017 Dec 2020
Responsible URL
Javier Ruiz-Hidalgo / Xavier Giró


MALEGRA, TEC2016-75976-R, financed by the Spanish Ministerio de Economía, Industria y Competitividad and the European Regional Development Fund (ERDF)



The goal of this project is to study and develop tools combining graph signal representation and processing ideas with machine learning technology. These tools will be used in the context of applications where the size and/or the heterogeneity of the data represent challenges of the Big Data era. The development of technologies related to the capture, storage, search, distribution, transfer, analysis and visualization of ever growing heterogeneous datasets entails tremendous difficulties. At the same time, these difficulties open new opportunities and this development has become a major trend in the field of Information and Communication Technology. The research performed in this project targets applications such as multi-view representations, video analysis, remote sensing for earth monitoring, person identification, health monitoring, medical imaging, genomics, etc.

The project has 4 major objectives. The first two objectives concentrate most of the development of theoretical and basic tools within the project. Within them, we will investigate the creation, analysis, segmentation, filtering and merging of graph structures of heterogeneous multimodal data and on the combination of these ideas with machine learning techniques. This combination with machine learning will be used for several different purposes. In particular, to provide a classification decision, to learn a mapping or a model to be used in a data processing architecture, to learn features that outperform handcrafted equivalents or to aggregate several features to create a signal to be further processed.

The last two objectives of the project focus on the application of the techniques and tools developed in the first two objectives in complex challenges that deal with big and heterogeneous data. In particular, these techniques and tools will be used to study the identification of persons in broadcast TV programs, the optimal encoding of depth maps in multi-view plus depth representations, the radiometric estimation and object detection in SAR and PolSAR images, the classification of multispectral and hyperspectral images, the understanding of brain changes during the evolution of Alzheimers disease, the inference of gene regulatory networks and the segmentation, tracking, indexing and super-resolution of multimodal video sequences.



Caros M, Garolera M, Radeva P, Giró-i-Nieto X. Automatic Reminiscence Therapy for Dementia. In: ACM International Conference on Multimedia Retrieval (ICMR). ACM International Conference on Multimedia Retrieval (ICMR). Dublin, Ireland: ACM; 2020. (4.37 MB)
Casamitjana A, Vilaplana V, Puch S, Aduriz A, Lopez C, Operto G, Cacciaglia R, Falcon C, Molinuevo JL, Gispert JD. NeAT: a nonlinear analysis toolbox for neuroimaging. Neuroinformatics. 2020 .
Gené-Mola J, Sanz R, Rosell-Polo JR, Morros JR, Ruiz-Hidalgo J, Vilaplana V, Gregorio E. Fruit detection and 3D location using instance segmentation neural networks and structure-from-motion photogrammetry. Computers and Electronics in Agriculture. 2020 ;169.
Wang L, Nie D, Li G, Puybareau E, Dolz J, Zhang Q, Wang F, Xia J, Wu Z, Chen J, et al. Benchmark on Automatic 6-month-old Infant Brain Segmentation Algorithms: The iSeg-2017 Challenge. IEEE Transactions on Medical Imaging. 2019 .
Petrone P, Casamitjana A, Falcon C, Cànaves MArtigues, Operto G, Cacciaglia R, Molinuevo JLuis, Vilaplana V, Gispert JD. Prediction of amyloid pathology in cognitively unimpaired individuals using voxelwise analysis of longitudinal structural brain MRI. Alzheimer's Research & Therapy. 2019 ;11(1).
Mosella-Montoro A, Ruiz-Hidalgo J. Residual Attention Graph Convolutional Network for Geometric 3D Scene Classification. In: IEEE Conference on Computer Vision Workshop (ICCVW). IEEE Conference on Computer Vision Workshop (ICCVW). Seoul, Korea: IEEE; 2019. (314.43 KB)
Herrera-Palacio A, Ventura C, Giró-i-Nieto X. Video Object Linguistic Grounding. In: ACM Multimedia Workshop on Multimodal Understanding and Learning for Embodied Applications (MULEA). ACM Multimedia Workshop on Multimodal Understanding and Learning for Embodied Applications (MULEA). Nice, France: ACM; 2019. (441.12 KB)
Mas I, Morros JR, Vilaplana V. Picking groups instead of samples: A close look at Static Pool-based Meta-Active Learning. In: ICCV Workshop - MDALC 2019. ICCV Workshop - MDALC 2019. Seoul, South Korea; 2019. (911.15 KB)
López-Palma M, Morros JR, Corbalán M, Gago J. Audience measurement using a top-view camera and oriented trajectories. In: IEEE IECON 2019. IEEE IECON 2019. Lisbon, Portugal; 2019. (523.13 KB)
Duarte A. Cross-modal Neural Sign Language Translation. In: Torres J, Giró-i-Nieto X Proceedings of the 27th ACM International Conference on Multimedia - Doctoral Symposium. Proceedings of the 27th ACM International Conference on Multimedia - Doctoral Symposium. Nice, France: ACM; 2019. (392.69 KB)