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A hybridized modified densenet deep architecture with CLAHE algorithm for humpback whale identification and recognition

  • 1205: Emerging Technologies for Information Hiding and Forensics in Multimedia Systems
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Abstract

Authenticity in multimedia information retrieval system is one such parameter which is of utmost in demand. One of such parameters is the biometric based authentication system, as seen in case of human the system proves its importance in almost each and every field. But in case of animals it becomes quite complex to recognize them using unique biometric parameter. This paper presents a modified 10 layered DenseNet deep learning framework which is implemented for the identification of Humpback whale, in an online challenge conducted by the kaggle, using biometric parameter. The proposed dense model minimizes the traditional limitation of vanishing gradient problem and outperforms as compared with other methods exists in the literature. This paper also proposed an hand free flow algorithm for ROI segmentation. In addition, the proposed methodology also uses CLAHE as a preprocessing method and focused on the feature re-usability with an optimized number of parameters used for recognition. During the experimentation, validation of the proposed model is found satisfactory with an accuracy of 93.21%. The test set results are provided by the kaggle with an AUC-ROC result over public and private scoreboard with an accuracy of 94.47% and 92.54% respectively. The comparative result with other deep methodologies suggests that the highest accuracy gained is 95.55% by Se-ResNet50 by some other learderboard and proposed model is found at second place.

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Notes

  1. All the source images were taken from Google images under the search of animal identification

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Acknowledgements

The author is thankful towards the host organization for providing the basic facilities to implement this project. The author is also thankful towards Google Colab for providing the GPU session and students of the U.G. from host organization in helping to implement this project.

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The author declares that there is no funding associated for this project.

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Authors and Affiliations

  1. Department of CSE & IT, Jaypee Institute of Information Technology, Noida, India

    Ankit Vidyarthi

  2. Department of CSE, Dr B R Ambedkar National Institute of Technology Jalandhar, Punjab, India

    Aruna Malik

Authors
  1. Ankit Vidyarthi
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  2. Aruna Malik
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Correspondence to Ankit Vidyarthi.

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Ethics Statement

The author of this manuscript confirms that: (i) Informed, written consent has been obtained from the relevant sources wherever is required; (ii) All procedures followed were in accordance with the ethical standards of the responsible committee on human experimentation (institutional and national) and with the Helsinki Declaration of 1964 and its later amendments. (iii) The approval and/or informed consent were not required for study as the dataset is collected from the Kaggle website, a well known data repository for worldwide data analytics problem and live challenges.

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Vidyarthi, A., Malik, A. A hybridized modified densenet deep architecture with CLAHE algorithm for humpback whale identification and recognition. Multimed Tools Appl 81, 19779–19793 (2022). https://doi.org/10.1007/s11042-021-11034-4

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  • DOI: https://doi.org/10.1007/s11042-021-11034-4

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