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Content Based Video Retrieval Using Convolutional Neural Network

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Intelligent Systems and Applications (IntelliSys 2018)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 868))

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Abstract

We present a content-based retrieval method for unconstrained video. To achieve the goal content based video retrieval we segmented the videos, detecting objects that match the human-defined interest. Object detection and classification is basic task in video analysis. For object detection we separate the foreground object from background and perform localization on each extracted frames from videos, and measure intensity histogram of 8-oriented frames and then perform haar-cascade, Gabor filter, active appearance model (AAM) and Convolutional Neural Network (CNN) algorithms get the authentic result. We used two datasets: Youtube and SegTek containing more than 2000 videos and using cluster computing to get the state-of-the-arts result of object detection and segmentations.

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Authors and Affiliations

  1. Faculty of Information Technology, University of Central Punjab, Lahore, Pakistan

    Saeed Iqbal, Adnan N Qureshi & Awais M. Lodhi

Authors
  1. Saeed Iqbal
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  2. Adnan N Qureshi
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  3. Awais M. Lodhi
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Corresponding author

Correspondence to Saeed Iqbal .

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Editors and Affiliations

  1. Faculty of Science and Engineering, Saga University, Saga, Japan

    Kohei Arai

  2. The Science and Information (SAI) Organization, Bradford, UK

    Supriya Kapoor

  3. The Science and Information (SAI) Organization, Bradford, UK

    Rahul Bhatia

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Iqbal, S., Qureshi, A.N., Lodhi, A.M. (2019). Content Based Video Retrieval Using Convolutional Neural Network. In: Arai, K., Kapoor, S., Bhatia, R. (eds) Intelligent Systems and Applications. IntelliSys 2018. Advances in Intelligent Systems and Computing, vol 868. Springer, Cham. https://doi.org/10.1007/978-3-030-01054-6_12

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