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Lambda-IVR: An Indexing Framework for Video Big Data Retrieval Using Distributed In-memory Computation in the Cloud

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Innovations in Smart Cities Applications Volume 4 (SCA 2020)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 183))

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Abstract

Feature indexing for video retrieval poses a significant hurdle for indexing due to three significant challenges. First, there are different types of features in varying nature, such as deep Convolutional Neural Network (CNN) features, handcrafted features, recognized text from the videos, and audio features, etc. Secondly, feature matching for those varying types of features requires different similarity measure methods. And thirdly, considering the Big-Data era the number of features to be indexed is enormous. To address these issues, in this paper, we present a lambda style distributed in-memory scale-out inverted-index based feature indexing framework for video retrieval, which operates as SaaS in the cloud. First, the video features are acquired, decoupled, and the visual features are encoded using an adaptation of an existing feature encoder with improvements. Secondly, the visual encoded features and the textual features are aggregated. Finally, the aggregated features are indexed and readily available for retrieval. Our framework supports incremental updates without the need to re-index the data and can serve enormous concurrent queries. Experimental results show that our framework performs reasonably well in terms of, accuracy, precision, and efficiency.

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Acknowledgment

This work was supported by the Institute for Information & communications Technology Promotion (IITP) grant funded by the Korea government (MSIT) (No. 2016-0-00406, SIAT CCTV Cloud Platform).

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Authors and Affiliations

  1. Department of Computer Engineering, Kyung Hee University, Yongin, South Korea

    Muhammad Numan Khan, Aftab Alam, Tariq Habib Afridi & Young-Koo Lee

  2. School of Computer Science and Communication Engineering, Jiangsu University, Zhenjiang, China

    Shah Khalid

Authors
  1. Muhammad Numan Khan
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  2. Aftab Alam
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  3. Tariq Habib Afridi
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  4. Shah Khalid
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  5. Young-Koo Lee
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Corresponding author

Correspondence to Young-Koo Lee .

Editor information

Editors and Affiliations

  1. Computer Engineering Department, Faculty of Sciences and Techniques, Tangier, Morocco

    Mohamed Ben Ahmed

  2. Computer Engineering Department, Faculty of Engineering, Karabük University, Karabük, Turkey

    İsmail Rakıp Karaș

  3. CICS.NOVA - Interdisciplinary Centre of Social Sciences, Polytechnic Institute of Castelo Branco, Castelo Branco, Portugal

    Domingos Santos

  4. Department of Sociology of Culture and Communication, Saint Petersburg State University, St. Petersburg, Russia

    Olga Sergeyeva

  5. Faculty of Sciences and Techniques, Association Méditerranéenne des Sciences et Technologies, Tangier, Morocco

    Anouar Abdelhakim Boudhir

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Khan, M.N., Alam, A., Afridi, T.H., Khalid, S., Lee, YK. (2021). Lambda-IVR: An Indexing Framework for Video Big Data Retrieval Using Distributed In-memory Computation in the Cloud. In: Ben Ahmed, M., Rakıp Karaș, İ., Santos, D., Sergeyeva, O., Boudhir, A.A. (eds) Innovations in Smart Cities Applications Volume 4. SCA 2020. Lecture Notes in Networks and Systems, vol 183. Springer, Cham. https://doi.org/10.1007/978-3-030-66840-2_105

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  • DOI: https://doi.org/10.1007/978-3-030-66840-2_105

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  • Print ISBN: 978-3-030-66839-6

  • Online ISBN: 978-3-030-66840-2

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