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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
kumar Pagrut, N., Ganguly, S., Jaiswal, V., Singh, C.: An overview on epizootic ulcerative syndrome of fishes in India: a comprehensive report. J. Entomol. Zool. Stud. 11(4), 1941–1943 (2017)
Suresh, A.J., Asha, P.: Human action recognition in video using histogram of oriented gradient (HOG) features and probabilistic neural network (PNN). Int. J. Innov. Res. Comput. Commun. Eng. 4(7), 13255–13263 (2016)
Burge, C.A., et al.: Climate change influences on marine infectious diseases: implications for management and society. Ann. Rev. Mar. Sci. 6, 249–277 (2014)
Lafferty, K.D., et al.: Infectious diseases affect marine fisheries and aquaculture economics. Ann. Rev. Mar. Sci. 7, 471–496 (2015)
Malik, S., Kumar, T., Sahoo, A.K.: Image processing techniques for identification of fish disease. In: IEEE 2nd International Conference on Signal and Image Processing (ICSIP), pp. 55–59 (2017)
Antony Seba1, P., Rama Subbu Laskhmi, S., Umamaheswari, P.: Fish recognition based on HOG feature extraction using SVM prediction. Int. J. Adv. Res. Comput. Commun. Eng. 6(5), 296–299 (2017)
Ansari, F.J.: Hand gesture recognition using fusion of SIFT and HoG with SVM as a classifier. Int. J. Eng. Technol. Sci. Res. IJETSR 4(9), 206–210 (2017)
Maity, U., Mukherjee, J.: Automated color logo recognition technique using color and hog features. Int. J. Comput. Appl. 170(2), 38–41 (2017)
Khan, H.A.: MCS HOG features and SVM based handwritten digit recognition system. J. Intell. Learn. Syst. Appl. 9(2), 21–33 (2017)
Babri, U.M., Tanvir, M., Khurshid, K.: Feature based correspondence: a comparative study on image matching algorithms. Int. J. Adv. Comput. Sci. Appl. 7(3), 206–210 (2016)
Dalal, N., Triggs, B., Schmid, C.: Human detection using oriented histograms of flow and appearance. In: ECCV 2006, pp. 428–441 (2010)
Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2005), pp. 886–893 (2005)
Fischler, M.A., Bolles, R.C.: Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Commun. ACM 24(6), 381–395 (1981)
Haiam, A., Abdul-Azim, H.A.: Human action recognition using trajectory based representation. Egypt. Inform. J. 16(2), 187–198 (2015)
Pooja, G., Revansiddappa, S.K.: Abnormal activity detection using HOG features and SVM classifier. Int. J. Adv. Res. Electr. Electron. Instrum. Eng. 5(4), 381–395 (2016)
Adetiba, E., Olugbara, O.O.: Lung cancer prediction using neural network ensemble with histogram of oriented gradient genomic features. Sci. J. 2015, 1–17 (2015)
Tian, S., Lu, S., Su, B., Tan, C.L.: Scene text recognition using co-occurrence of histogram of oriented gradients. In: Proceedings of the 2013 12th International Conference on Document Analysis and Recognition, Washington, DC, USA, pp. 912–916 (2013)
Bagum, N., Monir, M.S.: Present status of fish disease and economic losses due to incidence of disease in rural freshwater aquaculture. J. Innov. Dev. Strateg. (JIDS) 7(3), 48–53 (2013)
Lyubchenko, V., Matarneh, R., Kobylin, O.: Digital image processing techniques for detection and diagnosis of fish diseases. Int. J. Adv. Res. Comput. Sci. Softw. Eng. 6(7), 79–83 (2016)
Kumar, V., Roy, S., Meena, D.K., Sarkar, U.K.: Application of probiotics in shrimp aquaculture: importance, mechanisms of action, and methods of administration. Rev. Fish. Sci. Aquac. 24(4), 342–368 (2016)
Malik, S., Kumar, T., Sahoo, A.K.: A novel approach to fish disease diagnostic system based on machine learning. Adv. Image Video Process. 5(1), 49–57 (2017)
ShavetaMalik, T.K.: Various edge detection techniques on different categories of fish. Int. J. Comput. Appl. 135(7), 6–11 (2016)
Malik, S., Kumar, T., Sahoo, A.K.: Fish disease detection using HOG and FAST feature descriptor. Int. J. Comput. Sci. Inf. Secur. (IJCSIS) 15(5), 216–221 (2017)
Cai, Z., Yu, P., Liang, Y., Lin, B., Huang, H.: SVM-KNN algorithm for image classification based on enhanced HOG feature. In: Proceedings of the International Conference on Intelligent systems and Image Processing (2016)
Stella, X.A., Sujatha, N.: Performance analysis of GFE, HOG and LBP feature extraction techniques using kNN classifier for oral cancer detection. J. Netw. Commun. Emerg. 6(7), 50–56 (2016)
Niblack, W.: An Introduction to Digital Image Processing. Strandberg Publishing Company (1985)
Antony Seba, P., et al.: Fish recognition based on HOG feature extraction using SVM prediction. Int. J. Adv. Res. Comput. Commun. Eng. 6(5), 296–299 (2017)
Santra, A.K., Christy, C.J.: Genetic algorithm and confusion matrix for document clustering. IJCSI Int. J. Comput. Sci. Issues 9(1), 322 (2012)
Powers, D.M.W.: Evaluation: from precision, recall and F-measure to ROC: informedness, markendness & correlation. J. Mach. Learn. Technol. 2(1), 37–63 (2011)
Lopes, J.N.S., Gonçalves, A.N.A., Fujimoto, R.Y., Carvalho, J.C.C.: Diagnosis of fish diseases using artificial neural networks. IJCSI Int. J. Comput. Sci. Issues 8(6), 68–73 (2011)
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).
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-3-030-58817-5_54
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-58816-8
Online ISBN: 978-3-030-58817-5
eBook Packages: Computer ScienceComputer Science (R0)