Recent computer vision approaches assisted by Deep Learning (DL) techniques have shown unexpected advancements in solving problems that did not seem obvious to automatize – such as face recognition, lip reading, plate recognition, or lung cancer diagnosis. However, there has been limited efforts from Computer Vision community to tackle food image recognition.
Food image recognition is a big challenge for DL from many points of view: just consider how many dishes exist around the world; or how many names a dish can have! Do we have DL techniques to overcome this?
In this talk, we review the field of food image analysis within a new framework: uncertainty-aware multi-task food recognition. After discussing our methodology to advance in this direction, we comment potential research, as well as its social and economic impact. We explain that the DL and Computer Vision community can bring powerful tools for professionals and individuals to get aware not only of what people eat but also of how people eat; which undoubtedly will be a step forward towards better health and well-being.
1. Eduardo Aguilar, Marc Bolaños, Petia Radeva: Regularized uncertainty-based multi-task learning model for food analysis. J. Visual Communication and Image Representation 60: 360-370 (2019)
2. Md. Mostafa Kamal Sarker, Hatem A. Rashwan, Farhan Akram, Estefanía Talavera, Syeda Furruka Banu, Petia Radeva, Domenec Puig: Recognizing Food Places in Egocentric Photo-Streams Using Multi-Scale Atrous Convolutional Networks and Self-Attention Mechanism. IEEE Access 7: 39069-39082(2019)
3. Eduardo Aguilar, Beatriz Remeseiro, Marc Bolaños, Petia Radeva: Grab, Pay, and Eat: Semantic Food Detection for Smart Restaurants. IEEE Trans. Multimedia 20(12): 3266-3275 (2018)
4. Aina Ferrà, Eduardo Aguilar, Petia Radeva: Multiple Wavelet Pooling for CNNs. ECCV Workshops (4) 2018: 671-675
5.Talavera Martinez E, Leyva-Vallina M, Sarker MK, Puig D, Petkov N, Radeva P. "Hierarchical approach to classify food scenes in egocentric photo-streams.", IEEE J Biomed Health Inform. 2019 Jun 12. doi: 10.1109/JBHI.2019.2922390
Prof. Petia Radeva is a Full Professor in the Department of Mathematics and Computer Science at the Universitat de Barcelona (UB), Principal Investigator (PI) of the UB's consolidated research group for Computer Vision (CVUB, www.ub.edu/cvub) and Head of the Medical Imaging Laboratory of the Autonomous University of Barcelona's Computer Vision Center (www.cvc.uab.es).
She is currently the PI of UB in several European projects devoted to applying Computer Vision and Machine Learning to food intake monitoring (e.g. for patients with kidney transplants and for the elderly).
Petia Radeva has been a RIA-FET-OPEN vice-chair since 2015, a mentor in the Wild Cards EIT program, an associate editor of the Pattern Recognition Journal (Q1) and International Journal of Visual Communication and Image Representation (Q2), an IAPR Fellow since 2015, and acknowledged by ICREA Academia for her scientific merits since 2014.
She has received several international awards, including the Aurora Pons Porrata prize, the Antonio Caparrós Award for the best technology transfer, and more.
She has more than 90 SCI journal publications and 250 international chapters and proceedings, h-index - 42.
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