Towards Emotion Retrieval in Egocentric PhotoStream
This addresses the challenge of emotion retrieval for users of wearable cameras, though it appears incremental as it builds on existing sentiment recognition methods.
The paper tackles the problem of automatically assigning sentiment (positive, neutral, or negative) to events extracted from egocentric photostreams, achieving a classification accuracy of 75% with an 8% deviation on the test set.
The availability and use of egocentric data are rapidly increasing due to the growing use of wearable cameras. Our aim is to study the effect (positive, neutral or negative) of egocentric images or events on an observer. Given egocentric photostreams capturing the wearer's days, we propose a method that aims to assign sentiment to events extracted from egocentric photostreams. Such moments can be candidates to retrieve according to their possibility of representing a positive experience for the camera's wearer. The proposed approach obtained a classification accuracy of 75% on the test set, with a deviation of 8%. Our model makes a step forward opening the door to sentiment recognition in egocentric photostreams.