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Flow-Free Video Object Segmentation

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Computer Vision, Pattern Recognition, Image Processing, and Graphics (NCVPRIPG 2017)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 841))

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Abstract

Segmenting foreground object from a video is a challenging task because of large deformations of objects, occlusions, and background clutter. In this paper, we propose a frame-by-frame but computationally efficient approach for video object segmentation by clustering visually similar generic object segments throughout the video. Our algorithm segments object instances appearing in the video and then performs clustering in order to group visually similar segments into one cluster. Since the object that needs to be segmented appears in most part of the video, we can retrieve the foreground segments from the cluster having maximum number of segments. We then apply a track and fill approach in order to localize the object in the frames where the object segmentation framework fails to segment any object. Our algorithm performs comparably to the recent automatic methods for video object segmentation when benchmarked on DAVIS dataset while being computationally much faster.

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Author information

Authors and Affiliations

  1. Electrical Engineering, Indian Institute of Technology Gandhinagar, Gandhinagar, 382355, Gujarat, India

    Aditya Vora & Shanmuganathan Raman

Authors
  1. Aditya Vora
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  2. Shanmuganathan Raman
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Correspondence to Aditya Vora .

Editor information

Editors and Affiliations

  1. Indian Institute of Technology Mandi, Mandi, Himachal Pradesh, India

    Renu Rameshan

  2. Indraprastha Institute of Information Technology, New Delhi, India

    Chetan Arora

  3. Indian Institute of Technology, New Delhi, India

    Sumantra Dutta Roy

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Vora, A., Raman, S. (2018). Flow-Free Video Object Segmentation. In: Rameshan, R., Arora, C., Dutta Roy, S. (eds) Computer Vision, Pattern Recognition, Image Processing, and Graphics. NCVPRIPG 2017. Communications in Computer and Information Science, vol 841. Springer, Singapore. https://doi.org/10.1007/978-981-13-0020-2_4

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  • DOI: https://doi.org/10.1007/978-981-13-0020-2_4

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-0019-6

  • Online ISBN: 978-981-13-0020-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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