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Saliency-based classification of objects in unconstrained underwater environments

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Abstract

Exploration of the deep-sea underwater environment is a challenging and non-trivial task. Underwater vehicles used for the exploration of such environments capture videos continuously. The processing of these videos is a major bottleneck for scientific research in this area. This paper presents a methodology for the classification of the objects in the unconstrained underwater environments into two broad classes namely - man-made and natural. The classification of the objects is achieved using the saliency gradient based morphological active contour models. A bag of features acquired from the contours of the objects is used for the classification using various classifiers. Principal Component Analysis is used for the removal of redundancy in the feature set. The proposed features classify the objects present in the unconstrained underwater environment into a man-made/natural class using the proposed features. The results show that all the classifiers performed well; though KNN and ensemble subspace KNN, performed marginally better.

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Acknowledgments

Nitin Kumar is thankful to the CSIR-CSIO, Chandigarh for providing the funding and opportunity to carry out this work under the grant UnWaR. The authors gratefully acknowledge ONC for providing the underwater videos for this research work.

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Authors and Affiliations

  1. Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, India

    Nitin Kumar, H. K. Sardana & S. N. Shome

  2. CSIR-Central Scientific Instruments Organisation (CSIR-CSIO), Chandigarh, India

    H. K. Sardana & Vishavpreet Singh

  3. CSIR-Central Mechanical Engineering Research Institute (CSIR-CMERI), Durgapur, West Bengal, India

    S. N. Shome

Authors
  1. Nitin Kumar
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  2. H. K. Sardana
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  3. S. N. Shome
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  4. Vishavpreet Singh
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Corresponding author

Correspondence to H. K. Sardana.

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Appendix

Appendix

The appendix shows some examples of man-made objects used for training the classifiers.

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Kumar, N., Sardana, H.K., Shome, S.N. et al. Saliency-based classification of objects in unconstrained underwater environments. Multimed Tools Appl 79, 25835–25851 (2020). https://doi.org/10.1007/s11042-020-09221-w

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  • DOI: https://doi.org/10.1007/s11042-020-09221-w

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