5 research outputs found
Sentiment Recognition in Egocentric Photostreams
Lifelogging is a process of collecting rich source of information about daily
life of people. In this paper, we introduce the problem of sentiment analysis
in egocentric events focusing on the moments that compose the images recalling
positive, neutral or negative feelings to the observer. We propose a method for
the classification of the sentiments in egocentric pictures based on global and
semantic image features extracted by Convolutional Neural Networks. We carried
out experiments on an egocentric dataset, which we organized in 3 classes on
the basis of the sentiment that is recalled to the user (positive, negative or
neutral)
Serious Games Application for Memory Training Using Egocentric Images
Mild cognitive impairment is the early stage of several neurodegenerative
diseases, such as Alzheimer's. In this work, we address the use of lifelogging
as a tool to obtain pictures from a patient's daily life from an egocentric
point of view. We propose to use them in combination with serious games as a
way to provide a non-pharmacological treatment to improve their quality of
life. To do so, we introduce a novel computer vision technique that classifies
rich and non rich egocentric images and uses them in serious games. We present
results over a dataset composed by 10,997 images, recorded by 7 different
users, achieving 79% of F1-score. Our model presents the first method used for
automatic egocentric images selection applicable to serious games.Comment: 11 page
Analyzing First-Person Stories Based on Socializing, Eating and Sedentary Patterns
First-person stories can be analyzed by means of egocentric pictures acquired
throughout the whole active day with wearable cameras. This manuscript presents
an egocentric dataset with more than 45,000 pictures from four people in
different environments such as working or studying. All the images were
manually labeled to identify three patterns of interest regarding people's
lifestyle: socializing, eating and sedentary. Additionally, two different
approaches are proposed to classify egocentric images into one of the 12 target
categories defined to characterize these three patterns. The approaches are
based on machine learning and deep learning techniques, including traditional
classifiers and state-of-art convolutional neural networks. The experimental
results obtained when applying these methods to the egocentric dataset
demonstrated their adequacy for the problem at hand.Comment: Accepted at First International Workshop on Social Signal Processing
and Beyond, 19th International Conference on Image Analysis and Processing
(ICIAP), September 201
Analyzing First-Person Stories Based on Socializing, Eating and Sedentary Patterns
First-person stories can be analyzed by means of egocentric pictures acquired
throughout the whole active day with wearable cameras. This manuscript presents
an egocentric dataset with more than 45,000 pictures from four people in
different environments such as working or studying. All the images were
manually labeled to identify three patterns of interest regarding people's
lifestyle: socializing, eating and sedentary. Additionally, two different
approaches are proposed to classify egocentric images into one of the 12 target
categories defined to characterize these three patterns. The approaches are
based on machine learning and deep learning techniques, including traditional
classifiers and state-of-art convolutional neural networks. The experimental
results obtained when applying these methods to the egocentric dataset
demonstrated their adequacy for the problem at hand.Comment: Accepted at First International Workshop on Social Signal Processing
and Beyond, 19th International Conference on Image Analysis and Processing
(ICIAP), September 201