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, Hueto F, Puig S, Malvehy J, Vilaplana V. Uncertainty Estimation in Deep Neural Networks for Dermoscopic Image Classification. In: CVPR 2020, ISIC Skin Image Analysis Workshop. CVPR 2020, ISIC Skin Image Analysis Workshop. ; In Press.
Moliner E, Salgueiro L, Vilaplana V. Weakly Supervised Semantic Segmentation for Remote Sensing Hyperspectral Imaging. In: International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2020). International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2020). ; In Press.
Gonzalez-i-Calabuig M, Ventura C, Giró-i-Nieto X. Curriculum Learning for Recurrent Video Object Segmentation. In: ECCV 2020 Women in Computer Vision Workshop. ECCV 2020 Women in Computer Vision Workshop. ; In Press. (1.76 MB)
Salgueiro L, Marcello J, Vilaplana V. Super-Resolution of Sentinel-2 Imagery Using Generative Adversarial Networks. Remote Sensing. In Press .
Casamitjana A, Petrone P, Molinuevo JLuis, Gispert JD, Vilaplana V. Projection to Latent Spaces disentangles pathological effects on brain morphology in the asymptomatic phase of Alzheimer’s disease. Frontiers in Neurology, section Applied Neuroimaging. In Press .
Rey-Arena M, Guirado E, Tabik S, Ruiz-Hidalgo J. FuCiTNet: Improving the generalization of deep learning networks by the fusion of learned class-inherent transformations. Information Fusion. 2020 ;63(195). (366.22 KB)
Bellver M, Salvador A, Torres J, Giró-i-Nieto X. Mask-guided sample selection for Semi-Supervised Instance Segmentation. Multimedia Tools and Applications. 2020 . (2.2 MB)
Oriol B, Luque J, Diego F, Giró-i-Nieto X. Transcription-Enriched Joint Embeddings or Spoken Descriptions of Images and Videos. In: CVPR 2020 Workshop on Egocentric Perception, Interaction and Computing. CVPR 2020 Workshop on Egocentric Perception, Interaction and Computing. Seattle, WA, USA: arXiv; 2020. (96.79 KB)
Gené-Mola J, Sanz R, Rosell-Polo JR, Morros JR, Ruiz-Hidalgo J, Vilaplana V, Gregorio E. Fuji-SfM dataset: A collection of annotated images and point clouds for Fuji apple detection and location using structure-from-motion photogrammetry. 2020 ;Data in Brief(Vol. 30).
Giró-i-Nieto X. One Perceptron to Rule Them All: Language, Vision, Audio and Speech (tutorial). In: ACM International Conference on Multimedia Retrieval (ICMR) 2020. ACM International Conference on Multimedia Retrieval (ICMR) 2020. Dublin, Ireland: ACM; 2020. (313.96 KB)

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