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Cross Modal Retrieval for Different Modalities in Multimedia

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Computational Vision and Bio-Inspired Computing ( ICCVBIC 2019)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1108))

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Abstract

Multimedia data like text, image and video is used widely, along with the development of social media. In order, to obtain the accurate multimedia information rapidly and effectively for a huge amount of sources remains as a challenging task. Cross-modal retrieval tries to break through the modality of different media objects that can be regarded as a unified multimedia retrieval approach. For many real-world applications, cross-modal retrieval is becoming essential from inputting the image to load the connected text documents or considering text to choose the accurate results. Video retrieval depends on semantics that includes characteristics like graphical and notion based video. Because of the combined exploitation of all these methodologies, the cross-modal content framework of multimedia data is effectively conserved, when this data is mapped into the combined subspace. The aim is to group together the text, image and video components of multimedia document which shows the similarity in features and to retrieve the most accurate image, text, or video according to the query given, based on the semantics.

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Author information

Authors and Affiliations

  1. Computer Science and Engineering, Mar Athanasius College of Engineering, Kothamangalam, Ernakulam, 686666, Kerala, India

    T. J. Osheen & Linda Sara Mathew

Authors
  1. T. J. Osheen
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  2. Linda Sara Mathew
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Corresponding author

Correspondence to T. J. Osheen .

Editor information

Editors and Affiliations

  1. Department of CSE, RVS Technical Campus, Coimbatore, India

    S. Smys

  2. Faculty of Engineering, Faculdade de Engenharia da Universidade do Porto, Porto, Portugal

    João Manuel R. S. Tavares

  3. Faculty of Engineering, Aurel Vlaicu University of Arad, Arad, Romania

    Valentina Emilia Balas

  4. School of Computing, Tokyo Institute of Technology, Tokyo, Japan

    Abdullah M. Iliyasu

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✓ All authors declare that there is no conflict of interest.

✓ No humans/animals involved in this research work.

✓ We have used our own data.

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Osheen, T.J., Mathew, L.S. (2020). Cross Modal Retrieval for Different Modalities in Multimedia. In: Smys, S., Tavares, J., Balas, V., Iliyasu, A. (eds) Computational Vision and Bio-Inspired Computing. ICCVBIC 2019. Advances in Intelligent Systems and Computing, vol 1108. Springer, Cham. https://doi.org/10.1007/978-3-030-37218-7_19

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