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DUICM Deep Underwater Image Classification Mobdel using Convolutional Neural Networks

DUICM Deep Underwater Image Classification Mobdel using Convolutional Neural Networks

Manimaran Aridoss, Chandramohan Dhasarathan, Ankur Dumka, Jayakumar Loganathan
Copyright: © 2020 |Volume: 12 |Issue: 3 |Pages: 13
ISSN: 1938-0259|EISSN: 1938-0267|EISBN13: 9781799805632|DOI: 10.4018/IJGHPC.2020070106
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MLA

Aridoss, Manimaran, et al. "DUICM Deep Underwater Image Classification Mobdel using Convolutional Neural Networks." IJGHPC vol.12, no.3 2020: pp.88-100. http://doi.org/10.4018/IJGHPC.2020070106

APA

Aridoss, M., Dhasarathan, C., Dumka, A., & Loganathan, J. (2020). DUICM Deep Underwater Image Classification Mobdel using Convolutional Neural Networks. International Journal of Grid and High Performance Computing (IJGHPC), 12(3), 88-100. http://doi.org/10.4018/IJGHPC.2020070106

Chicago

Aridoss, Manimaran, et al. "DUICM Deep Underwater Image Classification Mobdel using Convolutional Neural Networks," International Journal of Grid and High Performance Computing (IJGHPC) 12, no.3: 88-100. http://doi.org/10.4018/IJGHPC.2020070106

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Abstract

Classification of underwater images is a challenging task due to wavelength-dependent light propagation, absorption, and dispersion distort the visibility of images, which produces low contrast and degraded images in difficult operating environments. Deep learning algorithms are suitable to classify the turbid images, for that softmax activation function used for classification and minimize cross-entropy loss. The proposed deep underwater image classification model (DUICM) uses a convolutional neural network (CNN), a machine learning algorithm, for automatic underwater image classification. It helps to train the image and apply the classification techniques to categorise the turbid images for the selected features from the Benchmark Turbid Image Dataset. The proposed system was trained with several underwater images based on CNN models, which are independent to each sort of underwater image formation. Experimental results show that DUICM provides better classification accuracy against turbid underwater images. The proposed neural network model is validated using turbid images with different characteristics to prove the generalization capabilities.

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