RETRACTED ARTICLE: Classification based on underwater degradation using neural network

Abstract

Images captured in underwater are degraded by the attenuation of light in water. The classification technique of underwater images based on various degradation has not been explored so far. The classification process is quite difficult due to the complex background of the underwater images. Generally multiple features are extracted to improve the classification accuracy. The significant feature selection plays a vital role in the classification process. In this work, the process is carried out in two steps namely, (i) first, the features of the underwater images are extracted, (ii) the extracted features are given as input to the neural network (NN) to classify these images into different classes of degradation. The efficiency of this classification technique is measured based on the accuracy and error percentage. The experimental results imply that NN performs well in case of 5 classes of degradation due to distinguishable features and produces an accuracy of about 100 percent.