If you use this dataset in your research we kindly ask you to reference the following paper and URL link of this website: M Řeřbek and T Ebrahimi New Light Field Image Dataset 8th International Conference on Quality of Multimedia Experience (QoMEX) Lisbon Portugal 2016 Dataset Food-5K This is a dataset containing 2500 food and 2500 non-food images for the task of food/non-food classification in our paper "Food/Non-food Image Classification and Food Categorization using Pre-Trained GoogLeNet Model" The whole dataset is

All publications ‒ MMSPG ‐ EPFL

An improved objective metric to predict image quality using deep neural networks P Akyazi T Ebrahimi Human Vision and Electronic Imaging 2019 2019 IST International Symposium on Electronic Imaging 2019 Human Vision and Electronic Imaging 2019 Burlingame California US 13 – 17 January 2019

Classifying Glycemic Index from Food Images with Convolutional Neural Networks Jailson P Januario Ello a B Guedes F abio S de Silva 1Grupo de Pesquisas em Sistemas Inteligentes Laboratorio de Sistemas Inteligentes Escola Superior de Tecnologia

As mentioned the image name has contained the class ID and image ID what we need do is to put the food images to food folder and non_food images to a non_food folder Firstly we are going to make new directories including config output source data parallel to Food-5k

The dataset contains five contents compressed at various bitrates using both off-the-shelf solutions and state-of-the-art algorithms Results of objective quality evaluation using popular image metrics are included as well as annotated subjective scores using three different methodologies and two types of visualization setups

Classifying Glycemic Index from Food Images with Convolutional Neural Networks Jailson P Januario Ello a B Guedes F abio S de Silva 1Grupo de Pesquisas em Sistemas Inteligentes Laboratorio de Sistemas Inteligentes Escola Superior de Tecnologia

Most recent publications ‒ MMSPG ‐ EPFL

Quality assessment of compression solutions for ICIP 2017 Grand Challenge on light field image coding I Viola T Ebrahimi 2018 2018 International Conference on Multimedia and Expo Workshops San Diego California USA July 23-27 2018 DOI : 10 1109

New Light Field Image Dataset Martin Reˇ ˇrabek and Touradj Ebrahimi Multimedia Signal Processing Group EPFL Lausanne Switzerland Abstract—Recently an emerging light field imaging technol-ogy which enables capturing full light information in a scene has

Home Conferences MM Proceedings MM '15 Multimodal Dataset for Assessment of Quality of Experience in Immersive Multimedia poster Multimodal Dataset for Assessment of Quality of Experience in Immersive Multimedia Share on Authors: Anne-Flore Nicole

Evangelos Alexiou has been working as a Doctoral Assistant in the Multimedia Signal Processing Group at EPFL since July 2016 under the supervision of Prof Touradj Ebrahimi His research interests include multimedia video compression and transmission image processing and communication systems

If you use this dataset in your research we kindly ask you to reference the following paper and URL link of this website: M Řeřbek and T Ebrahimi New Light Field Image Dataset 8th International Conference on Quality of Multimedia Experience (QoMEX) Lisbon Portugal 2016

The whole image set was split into a training set of 4 im-ages (referred to as p04 p14 p22 and p30) and a testing set of 6 images (referred to as p01 p06 p10 bike cafe and woman) Figure 1 provides an overview of the dataset This set of images was coded

An improved objective metric to predict image quality using deep neural networks P Akyazi T Ebrahimi Human Vision and Electronic Imaging 2019 2019 IST International Symposium on Electronic Imaging 2019 Human Vision and Electronic Imaging 2019 Burlingame California US 13 – 17 January 2019

Quality assessment of compression solutions for ICIP 2017 Grand Challenge on light field image coding I Viola T Ebrahimi 2018 2018 International Conference on Multimedia and Expo Workshops San Diego California USA July 23-27 2018 DOI : 10 1109

VALID: Visual quality Assessment for Light field Images

The dataset contains five contents compressed at various bitrates using both off-the-shelf solutions and state-of-the-art algorithms Results of objective quality evaluation using popular image metrics are included as well as annotated subjective scores using three different methodologies and two types of visualization setups

The Food-101 dataset is a classification dataset containing 101 food categories and 1 000 images for each one of these 101 food categories totaling up to 101 000 images Our method of evaluation involves randomly sampling an image and a recipe corresponding to each of the Food-101 categories

The 9th and probably last lunch meeting of our research group in 2008 took place on November 7th This lunch was organized by Jong-Seok LEE Thanks to his wife's kind efforts and contributions we could all enjoy some interesting and very tasty Korean food and

Food‐101 database including 101 food classes with 1000 image of each class is a popular dataset in food domain Bossard et al ( 2014 ) created the Food‐101 database and achieved an average accuracy of 50 76% for classification using traditional machine learning methods

Project 2 : Food-11 Classification Now that we can classify input image as food or nonfood we shall begin with classifying food images into different categories of dishes Dataset For this project we will utilise Food-11 dataset "This dataset contains 16643 food

Evangelos Alexiou has been working as a Doctoral Assistant in the Multimedia Signal Processing Group at EPFL since July 2016 under the supervision of Prof Touradj Ebrahimi His research interests include multimedia video compression and transmission image processing and communication systems

Project 2 : Food-11 Classification Now that we can classify input image as food or nonfood we shall begin with classifying food images into different categories of dishes Dataset For this project we will utilise Food-11 dataset "This dataset contains 16643 food

Evangelos Alexiou has been working as a Doctoral Assistant in the Multimedia Signal Processing Group at EPFL since July 2016 under the supervision of Prof Touradj Ebrahimi His research interests include multimedia video compression and transmission image processing and communication systems

20082 and the EPFL Image Quality Database3 for 2D im- ages Therefore we have created a stereoscopic image dataset with various contents captured with different acquisition pa-rameters Given that dataset we have conducted extensive subjective tests to study