Abstract
In image processing, the goal of feature extraction is to extract a set of effective features from the raw data. Feature extraction starts from an initial set of measured data and builds derived values i.e. features intended to be informative and Non-redundant. The paper is based on the novel feature extraction approach for the detection of Epizootic Ulcerative Syndrome (EUS) fish disease which is misidentified among people. The EHOG (Enhanced Histogram of Oriented Gradient) which is a proposed feature Extractor to extract the features or information. The paper discuss its comparison with other existing techniques with different parameters. The Evaluation results shows that the EHOG is better in every parameters and also gives better accuracy and efficiency of the model which recognizes the disease.
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Acknowledgment
The EUS disease images of fish have been collected from National Bureau of Fish Genetic Resources (NBFGR, Lucknow) and ICAR-Central Inland Fisheries Research Institute (CIFRI), Kolkata. Thanks to Dr. A.K Sahoo (CIFRI, Kolkata) and Dr. P.K Pradhan (NBFGR, Lucknow).
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Malik, S., Mire, A., Tyagi, A.K., Arora, V. (2020). A Novel Feature Extractor Based on the Modified Approach of Histogram of Oriented Gradient. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2020. ICCSA 2020. Lecture Notes in Computer Science(), vol 12254. Springer, Cham. https://doi.org/10.1007/978-3-030-58817-5_54
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