PSO and genetic modeling of deep features for road passibility analysis during floods

Naina Said, Aysha Nayab, Kashif Ahmad, Muhib Ullah, Touqir Gohar, Ala Al-Fuqaha

    Research output: Contribution to journalConference articlepeer-review

    1 Citation (Scopus)

    Abstract

    In recent years, social media outlets have been widely exploited for disaster analysis and retrieving relevant information. Social media information can help in several ways, such as finding the mostly affected areas and information on casualties and scope of the damage etc. In this paper, we tackle a specific facet of social media in natural disasters, namely the identification of passable routs in a flooded region. In detail, we propose several solutions for two relevant tasks, namely (i) identification of flooded and non-flooded images in a collection of images retrieved from social media, and (ii) identification of passable roads in a flooded region. To this aim, we mainly rely on existing deep models pre-trained on ImageNet and Places dataset, where the models pre-trained on ImageNet extract object specific and the ones pre-trained on places dataset extract scene-level features. In order to properly utilize the object and scene-level features, we rely on different fusion methods including Particle Swarm Optimization (PSO) and Genetic Modeling of the deep features in a late fusion manner. The evaluation of the proposed methods are carried out on the large-scale datasets provided for MediaEval-2018 benchmarking competition on Multimedia and Satellites. The results demonstrate significant improvement in the performance over the baselines.

    Original languageEnglish
    JournalIS and T International Symposium on Electronic Imaging Science and Technology
    Volume2020
    Issue number8
    DOIs
    Publication statusPublished - 26 Jan 2020
    Event2020 Imaging and Multimedia Analytics in a Web and Mobile World Conference, IMAWM 2020 - Burlingame, United States
    Duration: 26 Jan 202030 Jan 2020

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