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Unravel the Outlier Detection for Indian Ayurvedic Plant Organ Image Dataset

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Proceedings of Third International Conference on Computing, Communications, and Cyber-Security

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 421))

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Abstract

Image-based outlier detection has been a fundamental research problem for machine learning and computer vision researchers. This paper unravels the outlier detection process for the data preparation framework of the Indian Ayurvedic plant organ image dataset. While creating dataset the outlier images might get introduce due to human or device errors. Identification and rectification of such outlier images are crucial part for creating clean dataset. This paper evaluated and compared four well-known and state-of-the-art outlier detection algorithms, namely Isolation Forest, Local Outlier Factor, Histogram-Based Outlier Score, and One-Class Support Vector Machine for detecting the outliers from the dataset of Indian Ayurvedic plant organ images. For this experiment dataset containing 690 images of “Centella asiatica” was used and augmented to generate more image samples. In total, 21 morphological, geometric, color, and texture features have been extracted from each plant organ image. The experiment shows the isolation forest giving superior results with 91% accuracy, at the same time Histogram-Based Outlier Score proves to be the fastest in execution time.

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Acknowledgements

Authors extend their sincere thanks to Dr. Minubhai Purabia, retired Professor Department of Botany, South Gujarat University, for his continuous support and providing domain knowledge. We are thankful to Late Dr. Haresh L. Dhaduk from Anand Agriculture University for facilitating researchers with sample collection and support.

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Authors and Affiliations

  1. Gujarat Vidyapith, Ahmedabad, India

    Meera Kansara & Ajay Parikh

Authors
  1. Meera Kansara
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  2. Ajay Parikh
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Corresponding author

Correspondence to Meera Kansara .

Editor information

Editors and Affiliations

  1. Department of Computer Science, KIET Group of Institutions, Ghaziabad, Delhi, India

    Pradeep Kumar Singh

  2. Institute of Computer Science, Polish Academy of Sciences, Warsaw, Poland

    Sławomir T. Wierzchoń

  3. Department of Computer Science and Engineering, Nirma University, Ahmedabad, India

    Sudeep Tanwar

  4. Federal University of Piauí, Teresina, Brazil

    Joel J. P. C. Rodrigues

  5. Faculty of Mathematics and Informatics, Warsaw University of Technology, Warsaw, Poland

    Maria Ganzha

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Kansara, M., Parikh, A. (2023). Unravel the Outlier Detection for Indian Ayurvedic Plant Organ Image Dataset. In: Singh, P.K., Wierzchoń, S.T., Tanwar, S., Rodrigues, J.J.P.C., Ganzha, M. (eds) Proceedings of Third International Conference on Computing, Communications, and Cyber-Security. Lecture Notes in Networks and Systems, vol 421. Springer, Singapore. https://doi.org/10.1007/978-981-19-1142-2_33

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  • DOI: https://doi.org/10.1007/978-981-19-1142-2_33

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-19-1141-5

  • Online ISBN: 978-981-19-1142-2

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