Please use this identifier to cite or link to this item: http://hdl.handle.net/2445/144211
Title: Towards social pattern characterization in egocentric photo-streams
Author: Aghaei, Maedeh
Dimiccoli, Mariella
Canton-Ferrer, Cristian
Radeva, Petia
Keywords: Aprenentatge automàtic
Processament digital d'imatges
Machine learning
Digital image processing
Issue Date: Jun-2018
Publisher: Elsevier
Abstract: Following the increasingly popular trend of social interaction analysis in egocentric vision, this article presents a comprehensive pipeline for automatic social pattern characterization of a wearable photo-camera user. The proposed framework relies merely on the visual analysis of egocentric photo-streams and consists of three major steps. The first step is to detect social interactions of the user where the impact of several social signals on the task is explored. The detected social events are inspected in the second step for categorization into different social meetings. These two steps act at event-level where each potential social event is modeled as a multi-dimensional time-series, whose dimensions correspond to a set of relevant features for each task; finally, LSTM is employed to classify the time-series. The last step of the framework is to characterize social patterns of the user. Our goal is to quantify the duration, the diversity and the frequency of the user social relations in various social situations. This goal is achieved by the discovery of recurrences of the same people across the whole set of social events related to the user. Experimental evaluation over EgoSocialStyle - the proposed dataset in this work, and EGO-GROUP demonstrates promising results on the task of social pattern characterization from egocentric photo-streams.
Note: Versió postprint del document publicat a: https://doi.org/10.1016/j.cviu.2018.05.001
It is part of: Computer Vision and Image Understanding, 2018, vol. 171, p. 104-117
URI: http://hdl.handle.net/2445/144211
Related resource: https://doi.org/10.1016/j.cviu.2018.05.001
ISSN: 1077-3142
Appears in Collections:Articles publicats en revistes (Matemàtiques i Informàtica)

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