IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0191064.html
   My bibliography  Save this article

Emotional modelling and classification of a large-scale collection of scene images in a cluster environment

Author

Listed:
  • Jianfang Cao
  • Yanfei Li
  • Yun Tian

Abstract

The development of network technology and the popularization of image capturing devices have led to a rapid increase in the number of digital images available, and it is becoming increasingly difficult to identify a desired image from among the massive number of possible images. Images usually contain rich semantic information, and people usually understand images at a high semantic level. Therefore, achieving the ability to use advanced technology to identify the emotional semantics contained in images to enable emotional semantic image classification remains an urgent issue in various industries. To this end, this study proposes an improved OCC emotion model that integrates personality and mood factors for emotional modelling to describe the emotional semantic information contained in an image. The proposed classification system integrates the k-Nearest Neighbour (KNN) algorithm with the Support Vector Machine (SVM) algorithm. The MapReduce parallel programming model was used to adapt the KNN-SVM algorithm for parallel implementation in the Hadoop cluster environment, thereby achieving emotional semantic understanding for the classification of a massive collection of images. For training and testing, 70,000 scene images were randomly selected from the SUN Database. The experimental results indicate that users with different personalities show overall consistency in their emotional understanding of the same image. For a training sample size of 50,000, the classification accuracies for different emotional categories targeted at users with different personalities were approximately 95%, and the training time was only 1/5 of that required for the corresponding algorithm with a single-node architecture. Furthermore, the speedup of the system also showed a linearly increasing tendency. Thus, the experiments achieved a good classification effect and can lay a foundation for classification in terms of additional types of emotional image semantics, thereby demonstrating the practical significance of the proposed model.

Suggested Citation

  • Jianfang Cao & Yanfei Li & Yun Tian, 2018. "Emotional modelling and classification of a large-scale collection of scene images in a cluster environment," PLOS ONE, Public Library of Science, vol. 13(1), pages 1-20, January.
  • Handle: RePEc:plo:pone00:0191064
    DOI: 10.1371/journal.pone.0191064
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0191064
    Download Restriction: no

    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0191064&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pone.0191064?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Leonidas E. Bantis & Christos T. Nakas & Benjamin Reiser, 2014. "Construction of confidence regions in the ROC space after the estimation of the optimal Youden index-based cut-off point," Biometrics, The International Biometric Society, vol. 70(1), pages 212-223, March.
    2. Truelove, Heather Barnes, 2012. "Energy source perceptions and policy support: Image associations, emotional evaluations, and cognitive beliefs," Energy Policy, Elsevier, vol. 45(C), pages 478-489.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Jianfang Cao & Min Wang & Yanfei Li & Qi Zhang, 2019. "Improved support vector machine classification algorithm based on adaptive feature weight updating in the Hadoop cluster environment," PLOS ONE, Public Library of Science, vol. 14(4), pages 1-18, April.
    2. Sumin Park & Haemi Park & Jungho Im & Cheolhee Yoo & Jinyoung Rhee & Byungdoo Lee & ChunGeun Kwon, 2019. "Delineation of high resolution climate regions over the Korean Peninsula using machine learning approaches," PLOS ONE, Public Library of Science, vol. 14(10), pages 1-23, October.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Van Dael, Miet & Lizin, Sebastien & Swinnen, Gilbert & Van Passel, Steven, 2017. "Young people’s acceptance of bioenergy and the influence of attitude strength on information provision," Renewable Energy, Elsevier, vol. 107(C), pages 417-430.
    2. Čábelková, Inna & Strielkowski, Wadim & Streimikiene, Dalia & Cavallaro, Fausto & Streimikis, Justas, 2021. "The social acceptance of nuclear fusion for decision making towards carbon free circular economy: Evidence from Czech Republic," Technological Forecasting and Social Change, Elsevier, vol. 163(C).
    3. Cousse, Julia, 2021. "Still in love with solar energy? Installation size, affect, and the social acceptance of renewable energy technologies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 145(C).
    4. Inna Čábelková & Wadim Strielkowski & Irina Firsova & Marina Korovushkina, 2020. "Public Acceptance of Renewable Energy Sources: a Case Study from the Czech Republic," Energies, MDPI, vol. 13(7), pages 1-15, April.
    5. Wang, Fan & Gu, Jibao & Wu, Jianlin, 2020. "Perspective taking, energy policy involvement, and public acceptance of nuclear energy: Evidence from China," Energy Policy, Elsevier, vol. 145(C).
    6. Goda Perlaviciute & Linda Steg & Nadja Contzen & Sabine Roeser & Nicole Huijts, 2018. "Emotional Responses to Energy Projects: Insights for Responsible Decision Making in a Sustainable Energy Transition," Sustainability, MDPI, vol. 10(7), pages 1-12, July.
    7. Adimari, Gianfranco & To, Duc-Khanh & Chiogna, Monica & Scatozza, Francesca & Facchiano, Antonio, 2024. "Likelihood-type confidence regions for optimal sensitivity and specificity of a diagnostic test," Computational Statistics & Data Analysis, Elsevier, vol. 189(C).
    8. Contu, Davide & Strazzera, Elisabetta, 2022. "Testing for saliency-led choice behavior in discrete choice modeling: An application in the context of preferences towards nuclear energy in Italy," Journal of choice modelling, Elsevier, vol. 44(C).
    9. Contu, Davide & Strazzera, Elisabetta & Mourato, Susana, 2016. "Modeling individual preferences for energy sources: The case of IV generation nuclear energy in Italy," Ecological Economics, Elsevier, vol. 127(C), pages 37-58.
    10. Bauwens, Thomas & Devine-Wright, Patrick, 2018. "Positive energies? An empirical study of community energy participation and attitudes to renewable energy," Energy Policy, Elsevier, vol. 118(C), pages 612-625.
    11. Nicholas Smith & Anthony Leiserowitz, 2014. "The Role of Emotion in Global Warming Policy Support and Opposition," Risk Analysis, John Wiley & Sons, vol. 34(5), pages 937-948, May.
    12. Lombard, Andrea & Ferreira, Sanette, 2014. "Residents' attitudes to proposed wind farms in the West Coast region of South Africa: A social perspective from the South," Energy Policy, Elsevier, vol. 66(C), pages 390-399.
    13. Spampatti, Tobia & Hahnel, Ulf J.J. & Trutnevyte, Evelina & Brosch, Tobias, 2022. "Short and long-term dominance of negative information in shaping public energy perceptions: The case of shallow geothermal systems," Energy Policy, Elsevier, vol. 167(C).
    14. Cousse, Julia & Trutnevyte, Evelina & Hahnel, Ulf J.J., 2021. "Tell me how you feel about geothermal energy: Affect as a revealing factor of the role of seismic risk on public acceptance," Energy Policy, Elsevier, vol. 158(C).
    15. Russell, Aaron & Firestone, Jeremy, 2022. "More than a feeling: Analyzing community cognitive and affective perceptions of the Block Island offshore wind project," Renewable Energy, Elsevier, vol. 193(C), pages 214-224.
    16. Susanne Stoll-Kleemann & Susanne Nicolai & Philipp Franikowski, 2022. "Exploring the Moral Challenges of Confronting High-Carbon-Emitting Behavior: The Role of Emotions and Media Coverage," Sustainability, MDPI, vol. 14(10), pages 1-19, May.
    17. Han, Charles C., 2014. "Demarketing fear: Bring the nuclear issue back to rational discourse," Energy Policy, Elsevier, vol. 64(C), pages 183-192.
    18. Pérez Odeh, Rodrigo & Watts, David & Flores, Yarela, 2018. "Planning in a changing environment: Applications of portfolio optimisation to deal with risk in the electricity sector," Renewable and Sustainable Energy Reviews, Elsevier, vol. 82(P3), pages 3808-3823.
    19. Hartmann, Patrick & Apaolaza, Vanessa & D'Souza, Clare & Echebarria, Carmen & Barrutia, Jose M., 2013. "Nuclear power threats, public opposition and green electricity adoption: Effects of threat belief appraisal and fear arousal," Energy Policy, Elsevier, vol. 62(C), pages 1366-1376.
    20. Astrid Buchmayr & Luc Van Ootegem & Jo Dewulf & Elsy Verhofstadt, 2021. "Understanding Attitudes towards Renewable Energy Technologies and the Effect of Local Experiences," Energies, MDPI, vol. 14(22), pages 1-23, November.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pone00:0191064. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.