MALEGRA - Multimodal Signal Processing and Machine Learning on Graphs

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

Reference

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

 

Description

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.

 

Publications

Combalia M, Vilaplana V. Monte-Carlo Sampling Applied to Multiple Instance Learning for Histological Image Classification. In: Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support. Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support. Springer International Publishing; 2018. pp. 274-281.
Casamitjana A, Catà M, Sánchez I, Combalia M, Vilaplana V. Cascaded V-Net Using ROI Masks for Brain Tumor Segmentation. In: Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. BrainLes 2017. Vol. 10670. Crimi A., Bakas S., Kuijf H., Menze B., Reyes M. (eds). Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. BrainLes 2017. Cham: Springer; 2018. pp. 381-391.
Tochon G, Dalla Mura M, Veganzones MA, Valero S, Salembier P, Chanussot J. Advances in utilization of hierarchical representations in remote sensing data analysis. In: Reference Module in Earth Systems and Environmental Sciences. Vol. 2. Reference Module in Earth Systems and Environmental Sciences. Elsevier; 2018. pp. 77-107.
Luque B, Morros JR, Ruiz-Hidalgo J. Spatio-Temporal Road Detection from Aerial Imagery using CNNs. In: International Conference on Computer Vision Theory and Applications. International Conference on Computer Vision Theory and Applications. Porto, Portugal; 2017. (6.14 MB)
Salvador A, Baradad M, Giró-i-Nieto X, Marqués F. Recurrent Semantic Instance Segmentation. In: NIPS 2017 Women in Machine Learning Workshop (WiML). NIPS 2017 Women in Machine Learning Workshop (WiML). Long Beach, CA, USA: NIPS 2017 Women in Machine Learning Workshop; 2017. (1.15 MB)
Maceira M, Varas D, Morros JR, Ruiz-Hidalgo J, Marqués F. 3D hierarchical optimization for multi-view depth map coding. Multimedia Tools and Applications. 2017 . (4.23 MB)
Duarte A, Surís D, Salvador A, Torres J, Giró-i-Nieto X. Temporal-aware Cross-modal Embeddings for Video and Audio Retrieval. In: NIPS 2017 Women in Machine Learning Workshop (WiML). NIPS 2017 Women in Machine Learning Workshop (WiML). Long Beach, CA, USA: NIPS 2017 Women in Machine Learning Workshop; 2017. (155.1 KB)
Górriz M, Giró-i-Nieto X, Carlier A, Faure E. Cost-Effective Active Learning for Melanoma Segmentation. In: ML4H: Machine Learning for Health Workshop at NIPS 2017. ML4H: Machine Learning for Health Workshop at NIPS 2017. Long Beach, CA, USA; 2017. (521.82 KB)
Górriz M, Giró-i-Nieto X, Carlier A, Faure E. Active Deep Learning for Medical Imaging Segmentation. In: Medical Image meets NIPS 2017 Workshop. Medical Image meets NIPS 2017 Workshop. ; 2017. (187.43 KB)
Fernàndez D, Varas D, Espadaler J, Ferreira J, Woodward A, Rodríguez D, Giró-i-Nieto X, Riveiro JCarlos, Bou E. ViTS: Video Tagging System from Massive Web Multimedia Collections. In: ICCV 2017 Workshop on Web-scale Vision and Social Media . ICCV 2017 Workshop on Web-scale Vision and Social Media . Venice, Italy; 2017. (1.18 MB)

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