In this poster, we propose to face the problem of event detection from single images, by exploiting both background information often containing revealing contextual clues and details, which are salient for recognizing the event. Such details are visual objects critical to understand the underlying event depicted in the image and were recently defined in the literature as ”event-saliency”. Adopting the Multiple-Instance Learning (MIL) paradigm we propose a hierarchical approach analyzing first the entire picture and then refining the decision on the basis of the event-salient objects. Validation of the proposed method is carried out on two benchmarking datasets and it demonstrates the effectiveness of the proposed hierarchical approach to event discovery from single images.
Paper Details
- Authors:
- Submitted On:
- 7 December 2016 - 10:30am
- Short Link:
- Type:
- Poster
- Event:
- Presenter's Name:
- Alain Malacarne
- Paper Code:
- UCD-P1.2
- Document Year:
- 2016
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Document Files
GlobalSIP - A HIERARCHICAL APPROACH TO EVENT DISCOVERY FROM SINGLE IMAGES USING MIL FRAMEWORK.pdf
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url = {http://sigport.org/1409},
author = {Kashif Ahmad; Francesco De Natale; Giulia Boato; Andrea Rosani },
publisher = {IEEE SigPort},
title = {A HIERARCHICAL APPROACH TO EVENT DISCOVERY FROM SINGLE IMAGES USING MIL FRAMEWORK},
year = {2016} }
T1 - A HIERARCHICAL APPROACH TO EVENT DISCOVERY FROM SINGLE IMAGES USING MIL FRAMEWORK
AU - Kashif Ahmad; Francesco De Natale; Giulia Boato; Andrea Rosani
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/1409
ER -
