The paper addresses the problem of adverse events (natural disasters) recognition in user-generated images from social media, addressing the problem from two complementary perspectives. On one side, we aim to provide a comprehensive comparative analysis of different feature extraction and classification algorithms, relying on two different families of feature extraction algorithms, namely (i) Global features and (ii) Deep features. On the other hand, we demonstrate that the fusion of different feature extraction and classification strategies can outperform the single methods by jointly exploiting the capabilities of individual feature descriptors. The evaluation of the methods are carried out on two datasets, including a benchmark and a self-collected dataset.

A Comparative Study of Global and Deep Features for the Analysis of User-Generated Natural Disaster Related Images / Ahmad, Kashif; Sohail, Amir; Conci, Nicola; De Natale, Francesco. - (2018), pp. 1-5. (Intervento presentato al convegno 13th IEEE Image, Video, and Multidimensional Signal Processing Workshop, IVMSP 2018 tenutosi a Aristi Village, Zagori, Greece nel 10th-12th June 2018) [10.1109/IVMSPW.2018.8448670].

A Comparative Study of Global and Deep Features for the Analysis of User-Generated Natural Disaster Related Images

Ahmad, Kashif;Conci, Nicola;De Natale, Francesco
2018-01-01

Abstract

The paper addresses the problem of adverse events (natural disasters) recognition in user-generated images from social media, addressing the problem from two complementary perspectives. On one side, we aim to provide a comprehensive comparative analysis of different feature extraction and classification algorithms, relying on two different families of feature extraction algorithms, namely (i) Global features and (ii) Deep features. On the other hand, we demonstrate that the fusion of different feature extraction and classification strategies can outperform the single methods by jointly exploiting the capabilities of individual feature descriptors. The evaluation of the methods are carried out on two datasets, including a benchmark and a self-collected dataset.
2018
2018 IEEE 13th Image, Video, and Multidimensional Signal Processing Workshop (IVMSP) Proceedings
Piscataway, NJ
Institute of Electrical and Electronics Engineers Inc.
9781538609514
Ahmad, Kashif; Sohail, Amir; Conci, Nicola; De Natale, Francesco
A Comparative Study of Global and Deep Features for the Analysis of User-Generated Natural Disaster Related Images / Ahmad, Kashif; Sohail, Amir; Conci, Nicola; De Natale, Francesco. - (2018), pp. 1-5. (Intervento presentato al convegno 13th IEEE Image, Video, and Multidimensional Signal Processing Workshop, IVMSP 2018 tenutosi a Aristi Village, Zagori, Greece nel 10th-12th June 2018) [10.1109/IVMSPW.2018.8448670].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11572/226033
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