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

Gomez P, Mohedano E, McGuinness K, Giró-i-Nieto X, O'Connor N. Demonstration of an Open Source Framework for Qualitative Evaluation of CBIR Systems. In: ACM Multimedia. ACM Multimedia. Seoul, South Korea: ACM; 2018. (11.05 MB)
Xu Z, Vilaplana V, Morros JR. Action Tube Extraction based 3D -CNN for RGB-D Action Recognition. In: International Conference on Content-Based Multimedia Indexing CBMI 2018. International Conference on Content-Based Multimedia Indexing CBMI 2018. ; 2018. (3.09 MB)
Surís D, Duarte A, Salvador A, Torres J, Giró-i-Nieto X. Cross-modal Embeddings for Video and Audio Retrieval. In: ECCV 2018 Women in Computer Vision Workshop. ECCV 2018 Women in Computer Vision Workshop. Munich, Germany: Springer; 2018. (1.07 MB)
Mohedano E, McGuinness K, Giró-i-Nieto X, O'Connor N. Saliency Weighted Convolutional Features for Instance Search. In: Content-Based Multimedia Indexing - CBMI. Content-Based Multimedia Indexing - CBMI. La Rochelle, France: IEEE; 2018. (3.8 MB)
Duarte A, Camli G, Torres J, Giró-i-Nieto X. Towards Speech to Sign Language Translation. In: ECCV 2018 Workshop on Shortcomings in Vision and Language. ECCV 2018 Workshop on Shortcomings in Vision and Language. ; 2018. (142.48 KB)
Casamitjana A, Petrone P, Artigues M, Molinuevo JL, Gispert JD, Vilaplana V. Projection to Latent Spaces Disentangles Specific Cerebral Morphometric Patterns Associated to Aging and Preclinical AD. Alzheimer's & Dementia: The Journal of the Alzheimer's Association . 2018 ;14(7):P869-P870.
Petrone P, Casamitjana A, Falcon C, Artigues M, Operto G, Skouras S, Molinuevo JL, Vilaplana V, Gispert JD. Characteristic Brain Volumetric Changes in the AD Preclinical Signature. Alzheimer's & Dementia: The Journal of the Alzheimer's Association. 2018 ;14(7):P1235.
Assens M, McGuinness K, Giró-i-Nieto X, O'Connor N. PathGAN: Visual Scanpath Prediction with Generative Adversarial Networks. In: ECCV 2018 Workshop on Egocentric Perception, Interaction and Compution (EPIC). ECCV 2018 Workshop on Egocentric Perception, Interaction and Compution (EPIC). Munich, Germany: Springer; 2018. (3.78 MB)
Linardos P, Mohedano E, Chertó M, Gurrin C, Giró-i-Nieto X. Temporal Saliency Adaptation in Egocentric Videos. In: ECCV 2018 Workshop on Egocentric Perception, Interaction and Computing. ECCV 2018 Workshop on Egocentric Perception, Interaction and Computing. Munich, Germany: Extended abstract; 2018. (279.32 KB)
Gené-Mola J, Gregorio E, Guevara J, Auat F, Escolà A, Morros JR, Rosell-Polo JR. Fruit Detection Using Mobile Terrestrial Laser Scanning. In: AgEng 2018,. AgEng 2018,. Wageningen (Netherlands); 2018.

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