Publication

Social relation recognition in photostreams

Conference Article

Conference

IEEE International Conference on Image Processing (ICIP)

Edition

26th

Pages

3227-3231

Doc link

https://doi.org/10.1109/ICIP.2019.8803634

File

Download the digital copy of the doc pdf document

Authors

Abstract

This paper proposes an approach to automatically categorize the social interactions of a user wearing a photo-camera (2fpm), by relying solely on what the camera is seeing. The problem is challenging due to the overwhelming complexity of social life and the extreme intra-class variability of social interactions captured under unconstrained conditions. We adopt the formalization proposed in Bugental’s social theory, that groups human relations into five social domains with related categories. Our method is a new deep learning architecture that exploits the hierarchical structure of the label space and relies on a set of social attributes estimated at frame level to provide a semantic representation of social interactions. Experimental results on the new EgoSocialRelation dataset demonstrate the effectiveness of our proposal.

Categories

pattern recognition.

Author keywords

social relation recognition, egocentric vi-sion, multi-task learning, LSTM

Scientific reference

E. Sanchez, P. Radeva and M. Dimiccoli. Social relation recognition in photostreams, 26th IEEE International Conference on Image Processing, 2019, Taipei, Taiwan, pp. 3227-3231.